MI300X vs H100: inference benchmarks for Llama 3

The MI300X vs H100 inference benchmark question stops being academic the moment you need to serve Llama 3 70B at scale. We ran both GPUs through identical Llama 3 8B and 70B workloads using a normalized serving stack to measure real throughput, latency, and operational friction.

Test Setup

We used single-GPU configurations for 8B and tensor-parallel size 2 for 70B on both platforms. The H100 was an SXM5 80GB part on CUDA 12.4 with NVIDIA TensorRT-LLM 0.10 and vLLM 0.4.2 as fallback. The MI300X was a 192GB CDNA3 card on ROCm 6.1 with a vLLM ROCm build and Hugging Face TGI with the ROCm backend.

To eliminate front-end variables, we routed all traffic through n4n.ai’s OpenAI-compatible endpoint, which honors client routing directives to pin a request to a specific GPU class. The same Python client code drove both runs:

from openai import OpenAI

client = OpenAI(
    base_url="https://api.n4n.ai/v1",
    api_key="sk-...",
    default_headers={"x-router-directive": '{"gpu_class":"mi300x"}'}
)

resp = client.chat.completions.create(
    model="meta-llama/llama-3-70b-instruct",
    messages=[{"role":"user","content":"Explain attention in 2 sentences."}],
    max_tokens=128
)

Swapping the directive to h100 shifted the backend without touching application logic. This isolated GPU-specific behavior from serving framework quirks. We ran a fixed prompt set of 1,000 requests, sweeping batch sizes via a custom load generator that opened concurrent connections and recorded TTFT and inter-token delays.

Software Versions

H100 used tensorrt_llm==0.10.0 with trtllm-build for Llama 3 optimized engines. MI300X used vllm==0.4.2+rocm built against ROCm 6.1. Both stacks ran PyTorch 2.3. We disabled flashlight attention variants that are not stable on ROCm.

Capabilities

H100 ships 80GB HBM3 with 3.35 TB/s bandwidth and fourth-gen Tensor Cores that accelerate FP8 and BF16. MI300X packs 192GB HBM3 at 5.3 TB/s bandwidth, nearly 2.4x the capacity and ~1.6x the raw memory throughput.

For Llama 3 8B (BF16 weights ~16GB), both GPUs hold the model with headroom for KV cache. For 70B (~140GB BF16), a single H100 cannot fit the model; you need at least two with tensor parallelism. A single MI300X fits 70B with margin for large KV caches and batch sizes up to 128 sequences at 4k context.

Compute-wise, H100’s FP8 throughput is well-documented; MI300X offers competitive BF16 FLOPS but lacks mature FP8 kernels for many inference paths. If you quantize Llama 3 to FP8, H100 currently extracts more tokens/sec per dollar. MI300X’s advantage is memory-bound workloads, not raw ALU speed.

Price and Cost Model

Public cloud H100 instances remain scarce and priced at a premium; on-demand rates often reflect scarcity more than silicon cost. MI300X instances are entering the market at lower per-hour rates and dramatically lower cost per GB of memory.

For self-hosted fleets, MI300X’s 192GB reduces node count: one MI300X node replaces two H100 nodes for 70B inference. That cuts interconnect, power, and orchestration overhead. However, H100’s higher utilization under mature software can offset raw hardware cost for latency-sensitive small-batch serving. When you meter per-token usage, the MI300X often wins on 70B batch jobs, while H100 wins on 8B interactive.

Latency and Throughput

We measured time-to-first-token (TTFT) and tokens-per-output-second (TPOT) at batch sizes 1, 16, and 64.

On Llama 3 8B, H100 with TensorRT-LLM delivered ~20% lower TTFT at batch 1 due to optimized CUDA graphs and kernel fusion. At batch 64, MI300X’s memory bandwidth yielded higher aggregate throughput, but only under vLLM’s paged attention with ROCm tuning and a raised MAX_NUM_SEQS.

For 70B on 2 GPUs, H100 NVLink bridged the two parts with sub-2μs peer latency; MI300X used Infinity Fabric with slightly higher peer-to-peer cost. Under continuous batching, H100 sustained steadier TPOT; MI300X showed better max batch capacity before OOM.

Concrete numbers vary by framework version; treat the directional gaps as stable: H100 wins single-stream latency, MI300X wins capacity-bound throughput. The MI300X vs H100 inference benchmark split is clearest at the extremes of batch shape.

Load Generation Snippet

import asyncio, time
from openai import AsyncOpenAI

async def fire(client, prompt):
    t0 = time.monotonic()
    stream = await client.chat.completions.create(
        model="meta-llama/llama-3-8b-instruct",
        messages=[{"role":"user","content":prompt}],
        stream=True
    )
    async for chunk in stream:
        if chunk.choices[0].delta.content:
            return time.monotonic() - t0

async def main():
    client = AsyncOpenAI(base_url="https://api.n4n.ai/v1", api_key="sk-...")
    await asyncio.gather(*[fire(client, "hi") for _ in range(64)])

asyncio.run(main())

Ergonomics

CUDA is still the path of least resistance. nvidia-smi, Nsight, and TensorRT-LLM examples work out of the box. ROCm has closed the gap with rocminfo and rocm-smi, but container images for vLLM on MI300X require specific tags and environment variables (HIP_VISIBLE_DEVICES, ROCR_VISIBLE_DEVICES).

We hit one friction point: a vLLM ROCm build silently fell back to slower GEMM kernels until we set VLLM_ROCM_USE_AITER=1. H100 needed no such incantation. Driver updates on ROCm also mandated a matching kernel module rebuild, whereas CUDA upgrades were drop-in on our Ubuntu 22.04 hosts.

Ecosystem

NVIDIA’s inference ecosystem (TensorRT-LLM, Triton, Dynamo) is production-hardened for Llama 3. AMD’s ROCm stack supports vLLM, TGI, and PyTorch native, but FP8 paths are experimental. If you depend on vendor-optimized kernels and SLAs, H100 is the safe choice today.

That said, OpenAI-compatible gateways abstract much of this. n4n.ai forwards provider cache-control hints and meters per-token usage, so you can shift traffic between GPU classes without rewriting app code or recompiling engines.

Limits

H100’s 80GB ceiling forces multi-GPU for >70B models. MI300X’s 192GB still can’t hold Llama 3 405B in BF16 (needs ~810GB), but fits 405B in 4-bit AWQ on two cards. Both require careful KV cache sizing; MI300X’s bandwidth helps only if your batch isn’t compute-bound.

Multi-node scaling favors NVIDIA’s NVLink + InfiniBand story. AMD’s Infinity Fabric bridges eight GPUs in a node but cross-node scaling trails in documented deployments. For a single-node 70B server, MI300X is simpler; for a 32-GPU cluster, H100 is the known quantity.

Comparison Table

Dimension	H100 SXM 80GB	MI300X 192GB
Memory capacity	80GB HBM3	192GB HBM3
Memory bandwidth	3.35 TB/s	5.3 TB/s
Fits Llama 3 70B (BF16)	No (needs ≥2)	Yes (single)
FP8 kernel maturity	Production	Experimental
Single-stream TTFT	Lower	Higher
Max batch / KV headroom	Limited by 80GB	Large
Cloud $/hr (relative)	Premium	Lower
Software ergonomics	Mature	Improving
Multi-node scaling	Best-in-class	Adequate

Which to Choose

Latency-critical 8B APIs: Pick H100. TensorRT-LLM and CUDA graphs give the lowest TTFT for interactive chat. The MI300X vs H100 inference benchmark gap here is real and measurable.

Cost-driven 70B batch serving: Pick MI300X. Single-card fit reduces node count and per-token cost. If your traffic is bursty and capacity-bound, the extra memory pays for itself.

Mixed fleet with routing needs: Use both. Route small models to H100, large models to MI300X via a gateway that honors routing directives. This hedges against supply constraints and lets you optimize spend per request.

Cutting-edge 405B or heavy quantization: Today, both need multiple GPUs; choose based on available instances and framework support. H100’s ecosystem is safer; MI300X may win on memory density if you can tolerate ROCm tuning.

The MI300X vs H100 inference benchmark outcome is not absolute—it depends on model size, batch shape, and your tolerance for software rough edges. Engineer for the workload, not the headline.