Can You Run LLaMA 3 On-Premise? Hardware Requirements and Architecture

LLaMA 3 has become the default choice for enterprise private LLM deployments. But moving from "we want to run LLaMA 3 on-premise" to actually doing it requires answering specific hardware questions that many organizations struggle with. This article provides concrete specifications: exactly how much VRAM each model variant requires, which GPUs to buy, what quantization tradeoffs to accept, how to architect the serving stack, and whether the economics work compared to cloud APIs at your scale.

LLaMA 3 Model Sizes and VRAM Requirements

LLaMA 3 is available in three primary sizes: 8B, 70B, and 405B parameters. Each size targets different use cases and requires dramatically different hardware. The VRAM requirement is determined primarily by the number of parameters and the numerical precision used to store them.

LLaMA 3 8B

The 8B parameter variant is the most accessible. In FP16 (16-bit floating point), the model weights alone consume approximately 16 GB of VRAM. With KV cache overhead for inference, you need roughly 20-24 GB of VRAM for comfortable single-user serving and 24-32 GB for concurrent request handling.

This model fits on a single consumer GPU (RTX 4090 with 24 GB VRAM) or a single data center GPU (A100 40GB, L40S 48GB). With INT4 quantization, the model compresses to approximately 5 GB, making it viable even on mid-range hardware. The 8B variant is suitable for lightweight tasks: simple question answering, text summarization, classification, and basic chat applications where the quality requirements are not demanding.

LLaMA 3 70B

The 70B variant is the workhorse of enterprise deployments. In FP16, the model weights require approximately 140 GB of VRAM. With KV cache overhead for concurrent requests, plan for 160-180 GB total. This means a minimum of two A100 80GB GPUs or two H100 80GB GPUs with tensor parallelism.

With INT8 quantization, the model compresses to approximately 70 GB, fitting on a single A100 80GB or H100 80GB GPU. With INT4 quantization (AWQ or GPTQ), the model compresses to approximately 35-40 GB, fitting on a single A100 40GB or any 48GB+ GPU. INT4 quantization is the most common configuration for production deployments because it provides the best balance of quality preservation and hardware efficiency.

The 70B variant is the standard recommendation for enterprise use cases that require strong reasoning, instruction following, and multi-turn conversation. Its performance is competitive with GPT-4 on many enterprise tasks, particularly after fine-tuning on domain-specific data.

LLaMA 3 405B

The 405B variant is the frontier model of the LLaMA 3 family. In FP16, the model weights require approximately 810 GB of VRAM. Even with INT4 quantization, you need approximately 200-220 GB, requiring a minimum of three H100 80GB GPUs. A practical production deployment with headroom for KV cache and concurrent requests requires 4-8 H100 80GB GPUs depending on quantization level and expected throughput.

The 405B variant is justified only when the task demands approach frontier model capability and the organization has both the hardware budget and the operational expertise to manage a multi-node GPU deployment. For most enterprise use cases, the 70B variant (especially fine-tuned) provides comparable performance at a fraction of the hardware cost.

GPU Options for On-Premise Deployment

Selecting the right GPU involves balancing performance, VRAM capacity, availability, and cost. Here are the primary options for enterprise on-premise deployment in 2026.

NVIDIA H100 80GB

The gold standard for LLM inference. The H100 provides 80 GB of HBM3 memory with 3.35 TB/s memory bandwidth, which is the most critical specification for LLM inference performance. Memory bandwidth directly determines token generation speed because autoregressive decoding is memory-bandwidth-bound, not compute-bound.

A single H100 can serve LLaMA 3 70B in INT4 quantization at approximately 40-60 tokens per second for a single stream, or handle 8-12 concurrent requests with reasonable latency. At current market pricing, an H100 80GB costs approximately $25,000-$35,000 per unit. A two-GPU server with networking, storage, and chassis costs approximately $80,000-$120,000.

NVIDIA A100 80GB

The previous generation workhorse, still widely deployed and more readily available than H100s. The A100 provides 80 GB of HBM2e memory with 2.0 TB/s bandwidth. Inference performance is approximately 60-70% of the H100 due to lower memory bandwidth and fewer tensor cores.

The A100 remains a strong choice for organizations that can acquire them at favorable pricing (approximately $10,000-$15,000 per unit on the secondary market) or that already have A100 infrastructure from previous ML workloads. For LLaMA 3 70B in INT4, a single A100 80GB delivers approximately 25-40 tokens per second for a single stream.

NVIDIA L40S 48GB

A more affordable option designed for inference workloads. The L40S provides 48 GB of GDDR6X memory with 864 GB/s bandwidth. The lower memory bandwidth compared to HBM-based GPUs (A100, H100) results in significantly lower per-stream token generation speed, but the L40S compensates with a lower price point (approximately $7,000-$10,000 per unit).

The L40S can serve LLaMA 3 70B in INT4 quantization (the quantized model fits within 48 GB) but at lower throughput than HBM-based alternatives. It is best suited for lower-volume deployments or as a cost-effective option for development and testing environments.

AMD MI300X 192GB

AMD's entry into the LLM inference market offers a compelling VRAM specification: 192 GB of HBM3 per unit. This means a single MI300X can run LLaMA 3 70B in FP16 without quantization, preserving full model quality. Memory bandwidth is 5.3 TB/s, exceeding the H100. Pricing is competitive with H100 at approximately $20,000-$30,000 per unit. The primary tradeoff is software ecosystem maturity: vLLM and TGI support ROCm (AMD's CUDA alternative), but the ecosystem is less mature than NVIDIA's.

Quantization Tradeoffs

Quantization reduces the numerical precision of model weights, decreasing VRAM requirements at the cost of some quality degradation. Understanding the tradeoffs is critical for making the right deployment decision.

FP16 (No Quantization)

Full 16-bit floating point precision. Maximum model quality with no degradation. Requires the most VRAM (approximately 2 bytes per parameter). Use FP16 when you have sufficient VRAM and model quality is the top priority, such as applications involving complex reasoning, nuanced language generation, or tasks where you have validated that quantization measurably degrades performance on your specific evaluation set.

INT8 Quantization

Reduces model weights to 8-bit integers, cutting VRAM requirements roughly in half compared to FP16. Quality degradation is typically minimal and often undetectable in practical enterprise applications. INT8 is supported natively by modern GPU hardware (H100, A100) with dedicated INT8 tensor cores, so inference speed per token may actually improve compared to FP16 due to higher throughput on quantized operations.

INT8 is a safe default for most enterprise deployments. The quality loss is negligible for the vast majority of tasks, and the VRAM savings are substantial.

INT4 Quantization (AWQ, GPTQ)

Reduces model weights to 4-bit integers, cutting VRAM requirements to approximately one-quarter of FP16. This is where quality tradeoffs become measurable. On standard benchmarks (MMLU, HumanEval), INT4 quantized models typically score 1-3% lower than their FP16 counterparts. In practice, the impact depends heavily on the task. Simple Q&A, classification, and summarization tasks are minimally affected. Complex reasoning, mathematical computation, and tasks requiring precise factual recall may show more noticeable degradation.

AWQ (Activation-aware Weight Quantization) generally preserves quality better than GPTQ for the same bit width because it considers activation distributions during quantization. If using INT4, AWQ is the recommended approach for most enterprise deployments.

Serving Stack Architecture

A production on-premise LLM deployment requires more than just a GPU running a model. The complete serving stack includes several components working together.

Inference Engine: vLLM

vLLM is the standard inference engine for production LLM deployments. It implements PagedAttention for efficient GPU memory management, continuous batching for high throughput, and tensor parallelism for distributing large models across multiple GPUs. vLLM exposes an OpenAI-compatible REST API, which means any application built for the OpenAI API can point at your vLLM instance with minimal code changes.

A typical vLLM deployment command for LLaMA 3 70B INT4 on a single H100 looks like this: specify the model path, set the quantization method to AWQ, configure the tensor parallel size based on GPU count, set the maximum model length (context window), and configure the API server port. vLLM handles the rest: GPU memory allocation, request batching, and response streaming.

API Gateway

Place an API gateway (Kong, Envoy, Traefik, or cloud-native equivalents) in front of vLLM to handle authentication (JWT, API keys), rate limiting (per-user and per-team quotas), request logging (for audit and compliance), and TLS termination. The API gateway also provides a stable endpoint that persists across model updates and infrastructure changes.

Load Balancer

For multi-replica deployments, a load balancer distributes requests across vLLM instances. Standard HTTP load balancers work, but be aware that LLM requests have highly variable processing times (a short question might take 1 second while a long generation takes 30 seconds). Configure the load balancer for least-connections routing rather than round-robin to avoid overloading individual replicas with multiple long-running requests.

Monitoring Stack

Deploy Prometheus for metrics collection, Grafana for dashboards, and your existing alerting system (PagerDuty, OpsGenie) for notifications. Key metrics to monitor: GPU utilization and VRAM usage, tokens per second (throughput), time to first token (latency), request queue depth, and error rates. Set alerts for GPU utilization consistently above 85% (indicates capacity constraint), time to first token exceeding your SLA threshold, and any sustained error rate above 1%.

Networking and Storage Requirements

Beyond GPUs, two infrastructure components require specific attention:

Networking. Multi-GPU deployments using tensor parallelism require high-bandwidth, low-latency interconnects between GPUs. Within a single server, NVLink provides 900 GB/s bidirectional bandwidth. For multi-server deployments, InfiniBand (200-400 Gbps) is the standard. Standard 100GbE networking works but adds measurable latency for tensor-parallel inference across nodes. If possible, keep all GPUs for a single model within a single server to maximize interconnect performance.

Storage. Model weights for LLaMA 3 70B are approximately 140 GB (FP16) or 35 GB (INT4). Model loading time at startup depends on storage throughput. With NVMe SSDs providing 3-7 GB/s read speed, loading a 35 GB quantized model takes 5-10 seconds. With spinning disks or network storage, loading can take minutes. Use local NVMe storage for model weights to minimize startup and failover times.

Performance Benchmarks

Realistic performance expectations for LLaMA 3 70B across different hardware configurations, measured with continuous batching and realistic workload patterns:

• 1x H100 80GB, INT4 AWQ: 40-60 tokens/second single stream, 200-400 tokens/second aggregate with batching, supports 8-12 concurrent users with acceptable latency (under 3 seconds time-to-first-token).
• 2x H100 80GB, FP16 tensor parallel: 50-70 tokens/second single stream, 300-500 tokens/second aggregate, supports 15-25 concurrent users. Higher per-stream throughput than single-GPU INT4 due to full precision.
• 1x A100 80GB, INT4 AWQ: 25-40 tokens/second single stream, 150-250 tokens/second aggregate, supports 5-8 concurrent users. Approximately 60-65% of H100 performance.
• 2x L40S 48GB, INT4 AWQ (tensor parallel): 20-30 tokens/second single stream, 100-180 tokens/second aggregate, supports 4-6 concurrent users. Lower memory bandwidth is the primary bottleneck.

These numbers assume a mixed workload with average input length of 500 tokens and average output length of 300 tokens. Your actual performance will vary based on prompt length, generation length, and concurrency patterns.

Cost Analysis: On-Premise vs. Cloud API at Scale

The economic case for on-premise deployment depends on usage volume. Here is a side-by-side comparison at different scale points, assuming LLaMA 3 70B equivalent capability:

Low Volume (100K tokens/day)

Cloud API (e.g., OpenAI GPT-4o): At approximately $5 per million input tokens and $15 per million output tokens, 100K tokens per day costs roughly $30-$50 per month. On-premise hardware that sits mostly idle cannot compete at this scale. Use cloud APIs.

Medium Volume (10M tokens/day)

Cloud API: Approximately $3,000-$5,000 per month. On-premise (1x H100): Hardware amortized over 3 years plus power, cooling, and maintenance comes to approximately $3,000-$4,000 per month. At this scale, on-premise breaks even with cloud APIs. The decision rests on non-financial factors: data sovereignty, latency requirements, and customization needs.

High Volume (100M tokens/day)

Cloud API: Approximately $30,000-$50,000 per month. On-premise (2x H100): Approximately $5,000-$7,000 per month all-in. At this scale, on-premise is 5-8x cheaper than cloud APIs. The economics are overwhelming, and most organizations at this volume have already moved to self-hosted infrastructure.

Enterprise Scale (1B+ tokens/day)

Cloud API: $300,000-$500,000 per month. On-premise (8-16x H100): Approximately $25,000-$50,000 per month. At enterprise scale, on-premise deployment delivers 10-15x cost savings. The upfront capital investment pays for itself within 2-4 months of operation.

Running LLaMA 3 on-premise is not only feasible, it is well-understood engineering at this point. The hardware requirements are specific and predictable, the serving stack is mature, and the economics favor on-premise deployment for any organization processing more than approximately 10 million tokens per day. The decision to go on-premise should be driven by a combination of data sovereignty requirements, usage volume, and the strategic value of owning your AI infrastructure rather than renting it. For most enterprises handling sensitive data at meaningful scale, the answer is clear.