LLM.co

LLM.co

Private and custom large language models — the build, the boundaries and the bill. Fine-tuning versus retrieval, running models in your own environment, evaluation you can actually trust, data governance, and the questions to ask before a vendor answers them for you. Each episode takes one decision a team is facing — whether your problem needs a custom model at all, how to evaluate output without fooling yourself, what "private" has to mean contractually — and works it through concretely. Written for engineering and data leaders putting a model into production. Five or six minutes, one idea, no demos. Topics include fine-tuning versus retrieval, self-hosted and private deployment, evaluation you can trust, prompt and context design, data governance and retention, cost and latency tradeoffs, and what "private" has to mean contractually. Produced by LLM.co, private and custom large language models. Full details, services and further reading at https://llm.co

  1. 4h ago

    Which NVIDIA GPU Do You Actually Need for On-Prem LLMs?

    GPU selection for on-premises AI is widely treated as a hardware preference question, when it's really a memory budget problem with a straightforward formula. This episode of LLM.co walks through the GPU requirements guide for open-weight LLMs published by hardware.co, turning the model-by-model reference into an internalized framework any team can apply before they ever read a spec sheet. The episode covers the full sizing methodology and then applies it to the models teams are actually running in production today: The three-number formula: Weight memory at your chosen quantization, KV cache cost multiplied by your concurrent user count, and a 10–15% overhead buffer — add them up and your shortlist nearly writes itself. Quantization trade-offs in practice: FP8 is the most robust option across task types; 70B-scale models typically hold strong accuracy at four bits; smaller models can degrade noticeably at INT4. KV cache as the underweighted variable: A dense 70B model can consume 5 GB per request at 8K context or 80 GB at 128K — meaning KV cache, not weight size, often sets the real memory floor for multi-user deployments. Tier-by-tier GPU mapping: 7B–32B models on one or two L40S or RTX 6000 Ada cards; 70B class (e.g., Llama 3.3 70B) on two to four H100s for department-scale concurrency; flagship MoE models like DeepSeek V3/R1 and Qwen3-235B on 8× H100 or H200 nodes. MoE sizing traps: Only a fraction of parameters activate per token, but every weight must still be resident in VRAM — teams must plan for total parameter count, not the active slice. Notable outlier — gpt-oss-120b: OpenAI's Apache 2.0 MoE model, optimized for native MXFP4 on a single 80 GB GPU, is seeing fast on-prem uptake precisely because it breaks the usual multi-card requirement at 120B parameters. Teams unsure where their workload falls on this spectrum can use the AI Hardware Estimator to calculate VRAM requirements across models, quantizations, and user counts — and compare configurations against hardware.co's rack and cluster offerings. If the right hardware tier turns out to be a multi-card server build, the AI server configuration comparison page maps out four size options side by side. For more on the retrieval infrastructure that often sits alongside these models in regulated environments, the episode Pinecone, Qdrant, Weaviate, and pgvector for Regulated Enterprise RAG is a natural companion listen. hardware.co

  2. 4d ago

    Pinecone, Qdrant, Weaviate, and pgvector for Regulated Enterprise RAG

    For regulated enterprises building retrieval-augmented generation systems, the vector database shortlist isn't shaped by benchmarks — it's shaped by data residency requirements, security policy, and audit obligations. This episode of LLM.co works through the four stores that dominate serious RAG conversations right now — Pinecone, Qdrant, Weaviate, and pgvector — against the criteria that compliance teams actually apply. The full analysis behind the episode is available in the deep-dive article on vector stores for regulated enterprise RAG. The episode covers five decision layers that separate these engines once deployment context is fixed: Network isolation: Pinecone is a managed SaaS product — queries and vectors cross the customer boundary — while Qdrant, Weaviate, and pgvector all support fully self-hosted deployments that keep data inside the enterprise perimeter. For legal, defense, and many financial environments, this single fact ends the evaluation early. Row-level security: Regulated corpora are rarely uniform; patient subsets, client-privileged matter files, and ITAR-bucketed documents each require granular access enforcement at the engine level. pgvector inherits PostgreSQL's native CREATE POLICY semantics directly, making it the strongest candidate for HIPAA-compliant AI in healthcare, legal, and financial deployments where SQL policy is already the access-control standard. Hybrid search: Regulated search rarely stays purely semantic — case numbers, CPT codes, and ticker symbols demand keyword precision alongside vector recall. All four stores now offer some form of sparse-dense fusion, though the implementation differs: Weaviate uses tunable BM25-and-vector blending, Qdrant pairs sparse and dense vectors through a unified query API, and pgvector leverages PostgreSQL's native full-text search with fusion expressed in SQL. Scale and multi-tenancy: Weaviate's architecture is explicitly designed for one cluster serving thousands of logically isolated tenants simultaneously — a critical property for managed-service providers running private multi-document RAG per client. pgvector's headroom has expanded significantly with the pgvectorscale extension and iterative index scans introduced in the 0.8 release, with independent benchmarks showing materially lower latency and roughly one-fifth the monthly cost of Pinecone at matched recall on fifty million high-dimensional embeddings. Audit boundaries: SaaS vector stores export logs on the vendor's terms; self-hosted options write to local disk, journald, or Kafka and ingest into Splunk or Sentinel like any other on-premises workload — removing a vendor dependency that is difficult to defend to an auditor. The episode closes with a practical starting-point heuristic: teams already running PostgreSQL in production, with corpora under roughly fifty million vectors and a stated row-level security audit requirement, have a natural on-ramp in pgvector before evaluating more specialized engines. For more on the infrastructure considerations that sit alongside vector store selection, the episode The Numbers Behind On-Prem AI Hardware in 2026 covers the hardware economics of keeping AI workloads fully on-premises. LLM.co hardware.co

  3. Nov 8

    The Numbers Behind On-Prem AI Hardware in 2026

    On-premises AI deployment has quietly crossed a tipping point, and the statistics behind that shift are reshaping how enterprise technology leaders build their business cases. This episode of LLM.co digs into the data from the 2026 on-prem AI hardware statistics report — cutting through vendor-polished figures to surface the numbers that hold up when someone technical enters the room. From adoption trends to memory math to power and economics, the episode walks through the key metrics every infrastructure or IT leader should have at their fingertips before a procurement decision lands on a steering committee agenda: Adoption has flipped fast. On-prem and edge inference accounted for just 12% of enterprise AI workloads in 2023. By 2026, that share has reached 55% — with on-premises deployments now holding roughly 59% of LLM deployment share, concentrated in regulated industries. Security, not cost, is the dominant driver. Data privacy compliance ranks as the top LLM provider selection factor for 31% of enterprises, and 44% cite security as the single greatest barrier to AI adoption — making data residency and audit trails the most defensible arguments in a business case, not TCO projections alone. VRAM is the number that shapes everything else. The working rule is approximately 0.6 GB of VRAM per billion parameters at 4-bit quantization — meaning a 70B model needs ~42 GB in INT4, before accounting for KV cache. At 128K token context, that cache alone can add ~41 GB on top of model weights, a figure that catches many buyers off-guard. Teams exploring their own requirements can get a practical starting point with the AI hardware estimator for GPUs and VRAM. Power density is where budgets break. Traditional data center racks draw 7–10 kW. A modern AI rack draws 40–100 kW, and the most dense cluster hardware can demand up to ~142 kW in a single rack. Whether an existing facility can support that load — upstream feeds, transformer capacity, UPS — is a question that belongs in day one of the project plan, not as a late-stage footnote. Cooling thresholds follow density. Air cooling becomes inadequate above roughly 30–40 kW per rack; direct-to-chip liquid cooling handles up to ~150 kW; immersion covers 100–200 kW and above. Current cluster-class hardware sits squarely in liquid-only territory, making cooling method a design-phase decision, not a procurement-phase one. On-prem economics require honest utilization math. The break-even against cloud sits at consistent GPU utilization above 60–70% over three years, where on-prem can yield 30–50% long-term savings. Below that threshold, fixed hardware costs work against the investment — and the planning rule is simple: rent until projected workloads justify ownership. For more on building a resilient private AI infrastructure, the episode How to Audit and Fix Your Private AI Stack Before It Breaks in Production covers how to stress-test what you already have before committing to new hardware. If your workloads extend to document-heavy workflows at scale, it's also worth exploring Document AI for extraction, classification, and vision as a way to scope real memory and throughput requirements before sizing a system. hardware.co

  4. Nov 4

    How to Audit and Fix Your Private AI Stack Before It Breaks in Production

    RAND research puts the AI initiative failure rate above 80 percent — roughly twice that of comparable IT projects. For teams running private LLM deployments, that statistic is a prompt to act, not a headline to scroll past. This episode of LLM.co maps out a structured, hardware-up audit sequence that internal teams can execute before an outside firm — or a production incident — does it for them. The full methodology is detailed in the private AI stack audit and deficiency guide that inspired this discussion. The episode works through each layer of a private AI stack in the order that deficiencies actually compound, covering: Hardware inventory and thermal baselines — why GPU VRAM, interconnect fabric, KV-cache headroom at real context windows, and sustained-load power logs matter more than benchmark snapshots. Serving engine tuning — how to pull honest p50/p95/p99 latency at production concurrency, and why a stack that hasn't been retuned in a year is likely leaving significant throughput on the floor (MLPerf Inference data illustrates the gap). RAG and retrieval quality — using the Ragas Faithfulness metric and held-out query sets to catch confident, well-formatted hallucinations before users do; real-world hallucination rates from legal AI tools reframe what retrieval grounding actually guarantees. Agent layer risk controls — enumerating tool permissions, documenting kill switches, enforcing loop detection, and tracking cost per completed task; Gartner projects more than 40 percent of agentic projects canceled by 2027, and the episode explains the concrete controls that change that trajectory. Teams deploying autonomous workflows can also explore secure AI agent frameworks built for enterprise environments. Observability and the quiet unlogged path — why every request, retrieval call, and model completion must share one immutable trace ID, how to find fallback models and batch jobs that bypass the main pipeline, and how PII redaction must happen before spans leave the host. Regulatory mapping — aligning each finding to OWASP Top 10 for LLM Applications (2025 edition) and to sector-specific frameworks — HIPAA, GLBA, CMMC Level 2, SOC 2 — including how to document AI-specific risks that have no clean framework equivalent. For teams in regulated industries, LLM security audit and AI model review services can accelerate this process. The episode closes by emphasizing sequencing: hardware stability has to come first, because every metric measured on an unstable cluster is suspect. For more on a related infrastructure threat, listen to Why Your AI Server's Firmware Is the New Security Frontier, which covers the layer beneath the software stack that most audit checklists miss entirely. LLM.co hardware.co

  5. Nov 1

    Why Your AI Server's Firmware Is the New Security Frontier

    Most on-prem AI security discussions end at the network perimeter — firewalls, SSO, prompt logging, segmented VLANs. But a pair of high-profile research disclosures in late 2024 revealed a class of attack that bypasses all of it. This episode of LLM.co examines why AI server firmware has become the new security frontier, what the Eclypsium and HD Moore findings actually demonstrated, and how infrastructure leaders should respond when acquiring hardware to run large language models on-premises. The conversation covers the full attack surface hiding inside a modern AI server chassis — and what disciplined hardening looks like at each layer: The chassis is a stack of computers: The baseboard management controller (BMC) runs its own OS, boots before the hypervisor, and has full host control — yet sits largely outside conventional endpoint security tooling. DPUs and GPU firmware are live attack surfaces: A data processing unit with PCIe fabric access has a direct path to GPU memory; compromise at the VBIOS or UEFI level survives a full OS reinstall. Model weights are the prize: Weights loaded into high-bandwidth memory at inference time generally cannot be encrypted while live — exactly where the Eclypsium researchers demonstrated silent tampering, with traditional integrity checks returning clean results throughout. NIST SP 800-239 sets the baseline: The draft publication calls explicitly for hardware root of trust, confidential computing, and security-by-design across the data center lifecycle — including what happens to hardware after it leaves your facility. Hardening is not exotic: Isolating the management plane on a physically separate network, enforcing firmware signing across BMC, GPU VBIOS, and DPU, enabling IOMMU to restrict PCIe device visibility, and storing weight-encryption keys in a dedicated HSM are the core moves — and any serious vendor should be able to speak to them before you sign a purchase order. On-prem ownership cuts both ways: Running LLMs on your own production AI hardware means you can inspect and control firmware in ways a shared cloud tenancy never permits — but it also means the hardening responsibility lands squarely on your team. The episode also addresses decommissioning: returning an accelerator to a vendor with weights still resident is a data exposure event, not a logistics footnote. Procurement teams are encouraged to get data-destruction terms in writing before any hardware acquisition — a consideration worth raising early when using a vendor-neutral hardware sourcing process to evaluate AI server options. For more on the intersection of production AI systems and model improvement, the episode From Production Traces to Fine-Tuned Model: The Langfuse Training Loop is a natural companion listen. hardware.co

  6. Oct 25

    GPU Server Configs for Open-Weight LLMs: Match the Build to the Model

    Choosing the right GPU server for an open-weight LLM isn't as simple as headcount or budget — it comes down to model size, interconnect bandwidth, KV cache math, and concurrency requirements that only become visible once hardware is on a purchase order. This episode walks through the detailed guidance in the GPU server configuration guide for open-weight LLMs published on hardware.co, translating the key trade-offs into practical decision-making across four distinct build tiers. Here's what the episode covers: Start with the model, not the chassis. Four parameter bands — 7–14B, 24–34B, 70B dense, and large MoE — each carry specific VRAM, RAM, and interconnect requirements that should drive the server spec, not the other way around. VRAM and KV cache are the two numbers that matter most. Weights run ~2 bytes per parameter at BF16 and drop to ~0.5 bytes at INT4; KV cache scales with context length and concurrency and can easily exceed model weight storage at long-context serving loads. Single-GPU workstations are a legitimate production tier. An 80 GB card can serve a 70B model at 4-bit, a 32B at 8-bit, or any 7–14B at full precision — making it the right landing spot for most pilots and small-team deployments. Teams planning this kind of build can estimate their exact VRAM needs with the AI Hardware Estimator. Two-GPU builds live or die by their interconnect. Whether to run dual independent replicas, tensor-parallel a larger model, or dedicate one card to embeddings and reranking depends almost entirely on whether the cards share NVLink or are limited to PCIe peer-to-peer — a gap that can represent an order-of-magnitude difference in cross-GPU bandwidth. Four GPUs is the honest tier for 70B-class dense models and medium MoE. Mixtral's MoE architecture means you pay for ~46.7B parameters in VRAM but compute at roughly 13B-dense speeds — a useful planning target that explains why four-GPU configs suit MoE workloads so well. Eight-GPU nodes unlock frontier MoE and serious fine-tuning — but DeepSeek-V3's 671B total parameters require multi-node InfiniBand or RoCE setups, a parallel filesystem, and 8–12 kW of power capacity even before accounting for KV cache. For teams scaling to this level, a purpose-built GPU cluster for large models and high concurrency is worth evaluating. The episode makes clear that GPU count is the headline spec, but interconnect, system RAM, and NVMe storage are often where deployments succeed or fail. For more on the cost and security trade-offs of running models on your own hardware versus a managed service, check out the related episode Private LLM vs ChatGPT Enterprise: Cost, Security, and Performance. hardware.co

  7. Oct 21

    Private LLM vs ChatGPT Enterprise: Cost, Security, and Performance

    Choosing between a private LLM deployment and ChatGPT Enterprise is one of the most consequential AI infrastructure decisions a company can make right now — and most teams approach it with the answer already baked in. This episode works through the decision analytically, drawing on the full cost, security, and performance comparison published by LLM.co, and gives enterprise buyers a sharper framework for evaluating both paths on their actual merits. Here's what the episode covers: What each option actually is: ChatGPT Enterprise is a fully managed SaaS product — you never touch the model weights or inference stack. A private LLM means an open-weight model (Llama 3.3 70B, Qwen 2.5 72B, Mixtral, etc.) running on infrastructure your organization controls — on-premises, inside a VPC, or fully air-gapped. Where the cost crossover really is: ChatGPT Enterprise runs roughly $60 per seat per month with a minimum commitment, putting a 200-seat deployment at around $410K over three years. Private infrastructure carries higher upfront hardware costs but the per-user economics invert sharply at scale — by 2,000 seats, private can land well under half the SaaS cost over the same period. The decision is a utilization problem, not a sticker-price problem. What ChatGPT Enterprise's security controls actually cover — and where they stop: SOC 2 Type II, ISO 27001, SAML SSO, SCIM, admin-controlled retention, and a BAA for healthcare are all real and substantive. But prompts still leave your network, traverse OpenAI's infrastructure, and are stored in their tenancy. Enterprise Key Management protects stored ciphertext, not the inference path itself. Why regulated industries often can't accept that data flow: Law firms operating under privilege, hospitals bound by HIPAA, banks under GLBA, and defense contractors under CMMC 2.0 face data-residency and audit requirements that a private deployment satisfies by design — prompts stay in the VPC or on the rack, logs land in your own SIEM, and keys live in your own HSM. Teams with strict compliance obligations may also want to explore what a private LLM setup looks like for security-focused organizations. The real performance gap (and where it disappears): Frontier closed models like GPT-4o and GPT-5-class systems do lead on broad reasoning benchmarks. But on the narrow tasks most enterprise users actually run — document extraction, retrieval-grounded Q&A, code generation, classification — the delta is often small to zero. Fine-tuned open-weight models on a domain-specific corpus frequently outperform general-purpose frontier models on those tasks. Throughput and latency control: An eight-H100 node running vLLM can serve Llama 3.1 70B at roughly 2,800 tokens per second in aggregate — enough for hundreds of concurrent users at interactive speeds. With ChatGPT Enterprise, latency depends on OpenAI's load and routing, neither of which you can observe or influence. Organizations exploring fully self-hosted options can also look at LLM appliance deployments as a fast path to private infrastructure. For more from the show, check out Build Your Private AI Server in the Right Order — Starting with the Workload, which pairs well with this episode's infrastructure discussion. LLM.co hardware.co

  8. Oct 18

    Build Your Private AI Server in the Right Order — Starting with the Workload

    Ordering a GPU chassis before anyone has mapped the actual workload is one of the most expensive mistakes in enterprise AI infrastructure — and it happens constantly. This episode of LLM.co breaks down the correct sequence for building a private AI server, drawing directly from this step-by-step private AI server build guide published by hardware.co. Each decision in the chain flows from the one before it: workload first, then model, then VRAM, then GPU class, then chassis, then power and cooling, then software. The episode walks that full sequence, covering: Workload definition before any spec sheet: concurrent users, context window length, and whether the system needs inference only or training — a distinction that can shift memory requirements by an order of magnitude and dramatically change the hardware cost. Picking GPUs from the model, not the brochure: a 70-billion-parameter model like Llama 3.1 needs roughly 140–178 GB at full FP16 precision, but drops to 35–45 GB at INT4 quantization — a difference that determines whether you need two GPUs or eight. The AI Hardware Estimator for GPUs and VRAM is a practical tool for running this math before committing to a configuration. Quantization trade-offs in practice: INT8 halves memory with minimal quality loss; INT4 cuts memory by 75% and can nearly double inference throughput — but should always be validated against your own evaluation data before a rack is ordered. Interconnect bandwidth and its real-world impact: once a model spans more than one GPU, NVLink versus PCIe is the difference between usable latency and a model that stalls mid-response. Networking and storage planning before the rack arrives: the three storage tiers (fast NVMe for weights and cache, warm for embeddings, bulk for source data), and why undersizing NVMe is the most common cause of cold-start delays on day one. Software, governance, and power as deliberate choices: continuous batching (with throughput improvements of up to 23×), identity integration, prompt logging, EU AI Act compliance timelines, and the reality that a modern AI rack can draw 40–142 kW — far beyond what most enterprise facilities were designed to handle. For teams operating in regulated or data-sensitive environments, the AI Governance Software for audit and redaction covers the logging and compliance layer discussed in the episode. Listeners planning a healthcare deployment may also want to revisit Building a HIPAA-Compliant Private LLM: The Spec You Actually Need, which covers the compliance specification process in depth. hardware.co

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About

Private and custom large language models — the build, the boundaries and the bill. Fine-tuning versus retrieval, running models in your own environment, evaluation you can actually trust, data governance, and the questions to ask before a vendor answers them for you. Each episode takes one decision a team is facing — whether your problem needs a custom model at all, how to evaluate output without fooling yourself, what "private" has to mean contractually — and works it through concretely. Written for engineering and data leaders putting a model into production. Five or six minutes, one idea, no demos. Topics include fine-tuning versus retrieval, self-hosted and private deployment, evaluation you can trust, prompt and context design, data governance and retention, cost and latency tradeoffs, and what "private" has to mean contractually. Produced by LLM.co, private and custom large language models. Full details, services and further reading at https://llm.co

Information

  • Creator
    LLM.co
  • Years Active
    2026
  • Episodes
    20
  • Rating
    Clean
  • Copyright
    © 2026 LLM.co
  • Show Website
    LLM.co