AMD MI300X vs NVIDIA H100: Memory and Cost for AI Inference (2026)

AMD MI300X vs NVIDIA H100 is the comparison that actually matters for production AI inference right now, not the newer MI355X/H200 pairing that gets more headlines but far less real-world traffic. Both are the GPUs teams are deploying at scale today, and they disagree on almost everything: memory capacity, realized compute, interconnect bandwidth, and, depending on which pricing tracker you check, even which one costs less per hour.

MI300X's 192GB of HBM3 is real and it changes the math for models over 70B parameters, letting you skip tensor parallelism entirely on models that would otherwise need two or more GPUs. But H100 still wins decisively on realized training throughput, multi-node networking, and low-batch inference latency, mostly because of the gap between a 15-year-old CUDA ecosystem and a ROCm stack that's still catching up. This guide breaks down where each GPU actually wins, with sourced numbers instead of vendor marketing specs.

TL;DR: AMD MI300X vs NVIDIA H100, Memory and Cost

• Memory: AMD MI300X has 192GB of HBM3 per GPU against 80GB on NVIDIA H100 SXM5, so a 70B FP16 model fits on one MI300X but not on one H100.
• Price on Spheron: MI300X rents at $3.59/hr per GPU on-demand and H100 SXM5 at $2.65/hr, both as of 20 Sep 2026.
• Cost per token: Runpod's Mixtral 8x7B benchmark pricing (cited 6 Aug 2026) put MI300X at $22.22 per million tokens at batch size 1, H100 SXM at $28.11.
• Verdict: Runpod's Mixtral benchmark (6 Aug 2026) had MI300X cheaper per token at batch size 1 and 256+, H100 at batch 2 to roughly 128. H100 also leads on training and multi-node work. See H100 on Spheron.

Pricing fluctuates based on GPU availability. Spheron rates above are live as of 20 Sep 2026; other providers reflect their most recent published rates and may have changed. Check current GPU pricing → for live rates.

AMD MI300X vs NVIDIA H100 at a Glance

The short version: MI300X wins on memory capacity and per-GPU-hour price at some providers; H100 wins on realized compute throughput, networking, and low-batch latency. Neither GPU is a strict upgrade over the other, the right pick depends on your model size and batch profile.

Realized GEMM figures are from SemiAnalysis's independent benchmarking, not either vendor's marketed spec sheet. The gap between marketed and realized numbers is the entire reason to distrust a spec-table-only comparison.

192GB vs 80GB: What Extra HBM3 Buys You for 70B+ Models

More VRAM doesn't make a GPU faster. It changes how many GPUs you need to hold a model, and that changes your cost structure more than any TFLOPS number does.

Why Memory Capacity Beats Bandwidth for Large-Model Serving

H100 SXM5's HBM3 runs at 3.35 TB/s per GPU, still fast, but capped at 80GB. MI300X trades some of that bandwidth (5.3 TB/s, actually higher, not lower) for nearly 2.4x the capacity. For a memory-bound serving workload, the number that decides whether your model even fits on one card is capacity, not bandwidth. A model that spills across two GPUs pays a real tax: cross-GPU communication for every forward pass, more complex serving code, and double the hourly rental cost even if per-token compute needs didn't actually double.

Llama 3 70B and Mixtral 8x7B: Single-GPU vs Tensor-Parallel Math

Llama 3 70B in FP16 needs roughly 140GB just for weights, before KV cache. That doesn't fit on one 80GB H100, so H100 deployments either run tensor parallelism across two GPUs or quantize to INT8/FP8 to squeeze onto one card, with the quality tradeoffs that come with quantization. MI300X's 192GB holds the full FP16 weights plus a meaningful KV cache budget on a single GPU, no sharding, no cross-GPU all-reduce on every layer.

Mixtral 8x7B is smaller (around 93GB in FP16) but still tight on a single 80GB H100 once you add KV cache for any real context length. On MI300X it fits comfortably with room to spare for larger batches. The catch: fitting on one GPU and serving fast on one GPU are different claims, which is where the next section gets less favorable for MI300X.

Cost-Per-GB and Cost-Per-Token: Current Market Rates

The short version: MI300X gives you roughly 2.4x the memory per GPU, which usually beats H100 on cost-per-GB. Cost-per-token is closer and flips by batch size, MI300X wins at very low and very high batch sizes, H100 wins the middle range.

Per-GPU-Hour Pricing Across Providers (and Why Sources Disagree)

Here's where this comparison gets genuinely messy. Four different, independently checked pricing trackers (read 5-6 Aug 2026) disagree on which GPU is cheaper, so the table ends with a fifth source that prices both GPUs itself:

Spheron now lists MI300X on-demand at $3.59/hr per GPU, next to H100 SXM5 at $2.65/hr on-demand and $2.10/hr on spot, all as of 20 Sep 2026. That means you can rent MI300X and H100 on one account and price both sides of this comparison from the same source on the same day.

Pricing fluctuates based on GPU availability. Spheron rates above are live as of 20 Sep 2026; other providers reflect their most recent published rates and may have changed. Check current GPU pricing → for live rates.

The takeaway isn't "pick whichever number you like." It's that per-GPU-hour rate cards move fast and differ by provider, region, and commitment tier, so the only trustworthy comparison is pulling both quotes from the same source on the same day, not stitching together a MI300X number from one tracker and an H100 number from another. For the full provider-by-provider MI300X rate card, including spot and reserved tiers, see our MI300X and MI355X pricing guide. If you're specifically pricing Azure's MI300X instance, we cover that separately in our Azure MI300X pricing breakdown.

Even where MI300X looks cheaper on paper, MI300X's estimated purchase price runs around $15,000 to $18,000, and the buy-versus-rent breakeven lands around 9,045 hours, about 12.6 months of continuous 24/7 use. Below that usage level, renting wins regardless of which hourly number you're comparing.

Cost per Million Tokens by Batch Size (Where Each GPU Wins)

Runpod's Mixtral 8x7B inference benchmarks give the clearest batch-size breakdown available. At batch size 1, MI300X costs about $22.22 per million tokens against H100 SXM's $28.11, roughly 21% cheaper. That flips through the middle of the range: H100 SXM outperforms MI300X on both throughput and cost from roughly batch size 2 up through 128. Past 256, MI300X regains its cost advantage as its extra memory lets it hold larger batches without spilling.

So the honest answer to "which is cheaper per token" is: it depends entirely on where your traffic actually sits. A low-QPS endpoint serving one request at a time behaves nothing like a high-throughput batch pipeline, and the two GPUs trade places between them.

Pricing fluctuates based on GPU availability. Third-party rates and benchmark costs in this section are based on 6 Aug 2026 and may have changed. Check current GPU pricing → for live rates.

Where H100 Still Wins

Training Throughput and the CUDA/ROCm Software Gap

This is the section where MI300X's memory advantage stops mattering. SemiAnalysis's independently run GEMM benchmarks (not vendor marketing numbers) found H100/H200 hitting roughly 720 TFLOP/s BF16 against a marketed 989.5 TFLOP/s, while MI300X reached only about 620 TFLOP/s, roughly 14% slower despite MI300X's higher headline spec. The gap widens at FP8: H100/H200 realized about 1,280 TFLOP/s against MI300X's roughly 990 TFLOP/s, about 22% slower.

We saw that gap end to end rather than in isolated GEMMs. In our timed H100 vs MI300X LoRA benchmark, the same 375-step Llama 3.1 8B job took about 34 minutes on MI300X against about 18 on H100 on 13 Sep 2026, a per-step gap of roughly 33%, so Runpod's cheaper MI300X hourly rate that day still produced the more expensive job.

It gets worse on non-standard attention patterns. On Mistral 7B, which uses a non-causal sliding-window attention layer, the SemiAnalysis team found AMD's performance "close to half that of an H100" on public stable software releases. As the authors put it:

"The CUDA moat has yet to be crossed by AMD due to AMD's weaker-than-expected software Quality Assurance culture and its challenging out of the box experience."

Dylan Patel, Daniel Nishball, and Reyk Knuhtsen, SemiAnalysis

That's not a hardware limitation, it's a maturity gap in kernels, compiler tuning, and library support that shows up hardest on workloads outside the most common transformer patterns. Our side-by-side Triton kernel benchmark on MI300X and H100 isolates exactly that kernel-layer gap by running the identical, unmodified source on both stacks. For a deeper breakdown of where ROCm has closed ground and where it hasn't, see our ROCm vs CUDA guide. If you've already decided to train on AMD hardware anyway, our guide to training LLMs on MI300X/MI355X with ROCm covers the setup path this post doesn't.

Networking and Multi-Node Scaling (NVLink vs xGMI)

MI300X's xGMI interconnect runs point-to-point at 64 GB/s per GPU pair. H100's NVLink is switched and runs at 450 GB/s, roughly 7x higher. At the message sizes that actually show up in multi-node training and inference, 16 to 256 MiB, that gap translates directly into all-reduce operations running at roughly half the speed on MI300X clusters compared to H100. If your workload spans more than one node, this is usually the single biggest factor, bigger than either GPU's raw compute numbers. H100's form factor also matters here: NVLink bandwidth differs between SXM5, PCIe, and NVL variants, which we cover in our H100 NVL vs SXM5 vs PCIe guide.

Low-Batch, Latency-Sensitive Inference

At small batch sizes, H100 wins on both speed and cost. Runpod's benchmarks show H100 SXM outperforming MI300X across batch sizes 1 through roughly 128 on Mixtral 8x7B. At larger scale, dstack's benchmark comparing 8x H100 SXM5 against 4x MI300X at 1000 QPS peak load found the H100 setup processing about 74% more requests per second, with MI300X's time-to-first-token running more than twice as long. That comparison uses unequal GPU counts (8 vs 4), so it's not a clean per-GPU test, but it's a realistic picture of what a fixed hardware budget buys you: the 4x MI300X node actually has more total memory (768GB vs 640GB) and still loses on both throughput and latency at that concurrency level. For a real-time chat endpoint or any workload where P99 latency is the binding constraint, H100 is the safer default today.

When to Choose AMD MI300X vs NVIDIA H100

Use this as a quick filter, then verify against your own workload before committing budget:

Choose MI300X when:

• You're serving a 70B+ parameter model and want to avoid tensor parallelism entirely
• Your traffic runs at batch size 1 (low-QPS single-stream serving) or 256+ (high-throughput batch pipelines)
• You're memory-constrained rather than compute-constrained, and the provider you're checking has MI300X priced meaningfully below H100 that day

Choose H100 when:

• You're training, not just serving
• Your workload spans multiple nodes and needs fast all-reduce
• Your traffic sits in the batch size 2-128 range, which covers most real-time chat and agent workloads
• Latency (P50/P99 time-to-first-token) is a hard SLA, not a nice-to-have
• Your team's tooling is already built on CUDA and TensorRT-LLM

If your model is smaller than 70B and doesn't need MI300X's memory headroom in the first place, it's also worth checking whether an L40S beats both on cost per token for your batch profile, or whether H100 is simply overkill, which our best GPU for AI inference guide walks through model-size by model-size. For the wider NVIDIA lineup context, our A100 vs H100 comparison covers the generation below H100, and the full H100 datasheet has every precision tier and form factor if you need specs beyond what fits in the table above. If you land on MI300X after running your own numbers, test your real batch profile on a 1x or 2x Spheron instance before committing budget.

Both GPUs in this comparison now run on one Spheron account, so you can test MI300X and H100 SXM5 side by side at live rates instead of trusting a tracker.

Check MI300X availability → | On-demand H100 →