Best Platforms to Run Llama 3.3 Inference on NVIDIA H200 GPUs

Llama 3.3 70B is among the most widely deployed open-weight models in production in 2026. It matches GPT-4o class performance on most benchmarks, carries a permissive license for commercial use, and runs comfortably on a single NVIDIA H200 GPU at FP8 precision. The H200's 141 GB of HBM3e memory and 4.8 TB/s bandwidth are specifically what make that single-GPU deployment practical at production throughput.

• A single H200 fits Llama 3.3 70B at FP8 with room for KV cache and active batching. The same model requires two H100 80GB GPUs at FP8, or four at FP16. The H200 eliminates multi-GPU coordination overhead for this model class.
• Memory bandwidth drives Llama 3.3 throughput more than compute. The H200's 4.8 TB/s bandwidth versus the H100's 3.35 TB/s translates to a 1.4x to 1.9x token generation speedup for this memory-bound workload at comparable batch sizes.
• GMI Cloud runs H200 bare metal at $2.60/hr, with no hypervisor overhead, pre-installed TensorRT-LLM and vLLM, and a P99 latency of 180ms on Llama 3 70B FP8 in internal testing. The Inference Engine provides free access to Llama 3.3 70B Instruct Turbo with no credit card required.
• The cost-per-token advantage of the H200 over H100 compounds with scale. For Llama 3.3 70B specifically, moving from two H100s to one H200 eliminates tensor parallelism coordination overhead, halves the GPU-hour cost for the same effective throughput, and frees the second H100 for other workloads.
• Managed per-token APIs (Groq, Together AI, Fireworks AI) beat dedicated GPU economics below roughly 100 million tokens per month. Above that threshold, dedicated H200 infrastructure on a purpose-built platform consistently delivers lower cost-per-token and more predictable latency.
• Cold start latency is the overlooked production constraint. Loading Llama 3.3 70B FP16 from storage into VRAM takes 2 to 5 minutes. Platforms that keep the model resident in GPU memory eliminate this for standard endpoints. Self-hosted deployments require engineering the warm-pool strategy yourself.

Why Llama 3.3 and H200 Are a Natural Match

Llama 3.3 70B is a dense transformer model. Its inference decode phase is memory-bandwidth-bound rather than compute-bound: generating each new token requires loading approximately 70 GB of weights from VRAM once per forward pass at FP8. The constraint is how fast those weights can move from memory into compute units.

The H200 is specifically designed for this bottleneck. Its 4.8 TB/s HBM3e bandwidth represents a 1.4x increase over the H100's 3.35 TB/s, and its 141 GB capacity is 76 percent larger. For a model that fits in 70 GB at FP8 with room for a 71 GB KV cache and activation headroom, the H200 provides everything needed for high-throughput single-GPU serving at the 128K context window the model supports.

The practical consequence: teams that previously served Llama 3.3 70B on two H100s in tensor parallel configuration can move to a single H200 with equal or better throughput, half the GPU-hour spend, and no inter-GPU communication overhead.

Per NVIDIA's official H200 product brief benchmarks using TensorRT-LLM, FP8, and batch size 64 on Llama 2 70B (architecturally equivalent to Llama 3.3 70B for this purpose), 8x H200 delivers 34,864 tokens per second offline versus comparable H100 configurations. Independent cloud provider tests confirm 1.4x to 1.6x throughput gains under production loads.

What Actually Differentiates H200 Inference Platforms

Choosing an H200 platform for Llama 3.3 70B involves three engineering criteria that hourly pricing alone does not capture.

Virtualization overhead. Hypervisors (KVM, VMware, NVIDIA Nitro) intercept memory page operations and interrupt handling between the GPU and the workload. For LLM inference, where every token generation requires high-frequency memory access patterns, hypervisor overhead adds measurable latency to each forward pass. Bare metal instances eliminate this entirely. In testing running Llama 3 70B FP8, bare metal H200 instances deliver P99 latency of 180ms versus 215ms on comparable virtualized instances, a 35ms difference that is material for voice AI agents and real-time applications.

Pre-installed inference stack. Setting up TensorRT-LLM, vLLM, Triton Inference Server, and the correct CUDA, cuDNN, and NCCL versions for H200 hardware takes meaningful engineering time. Platforms that ship nodes pre-configured with the inference stack reduce deployment time from days to hours. The difference matters when evaluating platforms for proof-of-concept work before committing to production.

KV cache headroom. The H200's 141 GB provides 71 GB of headroom beyond the 70 GB FP8 weight footprint for Llama 3.3 70B. That headroom determines maximum batch size and usable context window under concurrent load. At 128K context with 32 concurrent requests, each request's KV cache uses approximately 0.31 MB per token times 128,000 tokens, consuming around 40 GB total per request in the worst case. Real production workloads use far shorter actual contexts, but the headroom still determines peak concurrency.

Platform-by-Platform Breakdown

GMI Cloud

GMI Cloud operates as an NVIDIA Reference Platform Partner with H200 infrastructure built exclusively for AI workloads. The architecture is bare metal first: no hypervisor sits between the workload and the H200 hardware.

H200 SXM instances run at $2.60/hr on-demand with per-minute billing. Nodes ship pre-configured with TensorRT-LLM, vLLM, and Triton Inference Server on CUDA 12.x, eliminating the environment setup phase. GPUDirect RDMA allows InfiniBand NICs to write directly to GPU memory without CPU involvement, reducing inference overhead for multi-GPU configurations.

For teams that need to evaluate before committing, the Inference Engine provides free access to Llama 3.3 70B Instruct Turbo with no credit card required. This endpoint runs on the same H100 and H200 production infrastructure, giving an accurate benchmark of what paid workloads will experience.

The progression from serverless to dedicated is frictionless on GMI Cloud. Teams start with the Inference Engine's per-request pricing, validate their workload, then move into dedicated H200 bare metal as traffic justifies it. The OpenAI-compatible API endpoint is identical across both tiers, so no application code changes are needed.

For sustained production serving at 10 million tokens per day, an H100 running Llama 3.3 70B FP8 with vLLM at continuous batching generates roughly 2,000 to 3,000 tokens per second. On an H200, the same configuration achieves 2,800 to 4,500 tokens per second given the bandwidth advantage. At $2.60/hr versus $2.00/hr for H100, the H200's throughput advantage delivers better cost-per-token for Llama 3.3 70B specifically.

Deploy Llama 3.3 on GMI Cloud H200

Hyperstack

Hyperstack provides H200 SXM on-demand at $2.40/hr and reserved from $1.90/hr. Deployment scales from 8 GPUs to 16,384 H200 SXMs, making Hyperstack the clearest option for teams that anticipate needing large H200 clusters as their Llama 3.3 workload grows.

InfiniBand networking is available for multi-node deployments. Managed Kubernetes support simplifies orchestration for teams running Llama 3.3 70B as part of a larger multi-model serving stack. Reserved pricing at $1.90/hr is among the lowest publicly listed H200 rates available, making Hyperstack competitive for teams that can commit utilization.

Together AI

Together AI hosts Llama 3.3 70B on managed GPU infrastructure at approximately $0.88 per million tokens (combined input and output). Dedicated H100 endpoints start at $3.99/hr, with 4 to 6 month reserved pricing from $2.25/hr.

For teams below the per-token to dedicated GPU crossover threshold, Together AI provides the most model diversity alongside Llama 3.3 70B. The 200-plus model catalog means routing decisions can shift between Llama 3.3 and alternatives like Qwen 2.5 72B or Llama 4 Maverick with a single parameter change. Fine-tuning on Llama 3.3 70B variants is supported, which no dedicated GPU rental provider offers as a managed service.

The practical ceiling is throughput predictability. Shared multi-tenant infrastructure makes P99 latency less consistent than dedicated bare metal under concurrent load.

Fireworks AI

Fireworks AI's FireAttention inference engine delivers up to four times lower latency than vLLM on H100 hardware through FP8 and FP16 optimization. Llama 3.3 70B is available on Fireworks' serverless infrastructure with dedicated endpoints for teams that need guaranteed capacity.

SOC 2 Type II and HIPAA compliance certification makes Fireworks the right choice for regulated industry workloads where Llama 3.3 70B fits the capability requirement and per-token pricing fits the volume. Dedicated endpoints provide the sub-second latency SLA that shared capacity cannot guarantee.

Groq

Groq's LPU hardware delivers 300 to 500 tokens per second on Llama 3.3 70B, with a median time-to-first-token of 65 milliseconds. No other provider on this list comes close on raw latency for interactive applications.

The free tier provides 30 requests per minute and 14,400 requests per day on Llama 3.3 70B, making it the standard starting point for latency benchmarking before committing to any paid infrastructure. The model catalog covers 15 to 20 models, so Groq fits best as the latency-optimized layer in a multi-provider stack rather than as a complete inference platform.

Nebius

Nebius provides H200 cloud infrastructure in Europe and Asia, with per-token pricing available on Llama 3.3 70B and NVIDIA Inception members eligible for up to $150,000 in cloud credits through the AI Lift program. For European teams requiring GDPR-compliant inference for Llama 3.3 70B workloads, Nebius is the most direct option with H200 hardware available within EU jurisdiction.

Cost-per-Token Across Deployment Approaches

The right platform for Llama 3.3 70B depends on monthly token volume and utilization predictability.

An H200 running Llama 3.3 70B FP8 with vLLM at batch size 32 delivers roughly 2,800 to 4,500 tokens per second. At $2.60/hr, effective cost per million output tokens runs approximately $0.16 to $0.26 at full utilization. Managed inference APIs at $0.88 per million output tokens are more economical below roughly 25 to 30 percent GPU utilization on dedicated hardware.

The inflection point where dedicated H200 infrastructure beats managed API pricing falls at approximately 80 to 100 million tokens per month under realistic utilization assumptions. Below that threshold, per-request pricing avoids paying for idle GPU time. Above it, the fixed GPU-hour cost with batching efficiency produces lower effective cost per token.

Serving Stack Recommendations for Llama 3.3 70B on H200

vLLM is the default choice for most teams. OpenAI-compatible API, continuous batching, PagedAttention, FP8 support on H200, and the broadest hardware compatibility. An H200 running vLLM with FP8 quantization and continuous batching at batch size 32 delivers 2,000 to 3,000 tokens per second throughput on Llama 3.3 70B.

TensorRT-LLM delivers 15 to 30 percent higher peak throughput than vLLM after a 10 to 30 minute compilation step per model configuration. The compilation investment makes sense for production deployments where the model will not change frequently and peak throughput is the primary constraint. GMI Cloud nodes ship with TensorRT-LLM pre-installed.

SGLang provides a 29 percent throughput advantage over vLLM on H200 hardware generally, and specific optimizations for multi-turn workloads through RadixAttention KV cache reuse. For Llama 3.3 70B in RAG applications with shared system prompts, SGLang's prefix caching delivers meaningful throughput improvements. The DeepSeek-specific optimizations in SGLang are less relevant here than for MoE models, but the general architecture advantages apply.

Key configuration settings for Llama 3.3 70B on a single H200:

• Quantization: FP8 (70 GB weight footprint, leaving 71 GB for KV cache and batching)
• Tensor parallelism: 1 (single GPU, no multi-GPU coordination overhead)
• Max batch size: calibrate to KV cache headroom. At 4K average context, batch sizes of 32 to 64 are practical
• Context window: full 128K supported at FP8 on a single H200 for moderate concurrency

Conclusion

The H200 is the right hardware for Llama 3.3 70B production inference because it eliminates the two-GPU coordination requirement that H100 deployments require. Single-GPU simplicity, 1.4x to 1.9x throughput advantage, and 71 GB of headroom beyond the model's weight footprint for KV cache and batching make it the natural fit for this model class.

For teams ready to move from per-token API billing to dedicated infrastructure, GMI Cloud's H200 bare metal at $2.60/hr provides the best combination of performance, pricing, and inference-first architecture available. Bare metal access, pre-installed inference stack, no egress fees, and a free Llama 3.3 70B Instruct Turbo endpoint for benchmarking make it the starting point for any team evaluating H200 infrastructure for this workload.

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FAQs

Why is the H200 better than the H100 for Llama 3.3 70B inference specifically? Llama 3.3 70B's decode phase is memory-bandwidth-bound: generating each token requires loading approximately 70 GB of model weights from VRAM on every forward pass. The H200's 4.8 TB/s HBM3e bandwidth versus the H100's 3.35 TB/s translates directly to 1.4x to 1.9x faster token generation for this model. The H200's 141 GB capacity also allows Llama 3.3 70B to run on a single GPU at FP8, whereas the H100's 80 GB requires two GPUs in tensor parallel configuration, adding coordination overhead and doubling the GPU-hour cost per node.

How does Llama 3.3 70B fit in memory on an H200 at different precision levels? At FP8, Llama 3.3 70B requires approximately 70 GB for model weights, leaving 71 GB of the H200's 141 GB for KV cache and activations. This is sufficient for production serving at realistic context lengths and batch sizes. At FP16, weight storage requires approximately 142 GB, which barely fits within the 141 GB H200 capacity with no headroom for KV cache, making FP16 on a single H200 impractical for production. At INT4, the weight footprint drops to roughly 35 to 40 GB, fitting within a single H100 80 GB with meaningful KV cache headroom, though with measurable quality tradeoffs on reasoning and precise factual tasks.

At what token volume does a dedicated H200 beat per-token API pricing for Llama 3.3 70B? The crossover depends on GPU utilization. A dedicated H200 at $2.60/hr running Llama 3.3 70B at 70 percent utilization delivers roughly 2,000 tokens per second, producing approximately $0.36 per million output tokens in effective cost. Managed APIs like Together AI charge $0.88 per million output tokens for Llama 3.3 70B. At 50 percent utilization, dedicated H200 costs around $0.53 per million output tokens, still below the managed API rate. The practical crossover where dedicated infrastructure wins on cost sits at approximately 80 to 100 million output tokens per month, assuming 50 to 70 percent sustained utilization.

What is the difference between GMI Cloud's Inference Engine and dedicated H200 instances for Llama 3.3 70B? The Inference Engine is GMI Cloud's serverless inference layer. It hosts Llama 3.3 70B Instruct Turbo with automatic request batching, latency-aware scheduling, and scaling to zero between requests, with per-request pricing and no GPU provisioning required. It is the right starting point for variable-traffic workloads below 100 million tokens per month. Dedicated H200 bare metal instances give root access to physical H200 servers at $2.60/hr, with custom serving stack configuration, full control over quantization, batch size tuning, and model variants. Dedicated instances suit sustained high-volume workloads where utilization justifies the fixed GPU-hour cost and where serving stack control matters. The OpenAI-compatible API is identical across both deployment paths.

How does Llama 3.3 70B performance on H200 compare to Groq's LPU inference? Groq's LPU hardware achieves 300 to 500 tokens per second for Llama 3.3 70B with a median time-to-first-token of 65 milliseconds, which is faster than any GPU-based provider at low concurrency and single-request latency. An H200 running Llama 3.3 70B with continuous batching at batch size 32 generates 2,800 to 4,500 tokens per second total throughput, significantly higher than Groq at scale, but distributed across many concurrent requests. For single-request interactive applications where the user feels every millisecond of latency, Groq is the faster option. For production serving of many concurrent users where total throughput and cost-per-token matter more than single-request latency, H200 infrastructure on GMI Cloud delivers better economics at scale.