Short answer: NVIDIA B300 and AMD Instinct MI355X occupy the same marketed 288GB HBM3E, 8TB/s accelerator class, but they are not interchangeable. B300 is the CUDA/Blackwell Ultra path and NVIDIA’s HGX datasheet lists 270GB usable with 7.7TB/s and up to 1100W. MI355X is the ROCm/CDNA 4 path with 288GB, 8TB/s and a 1400W direct-liquid-cooled design. Choose the complete supported platform that runs your exact model and kernels—not the larger vendor FLOPS number.
Neither is a workstation GPU. B300 is an SXM-class Blackwell Ultra accelerator for qualified HGX/DGX systems. MI355X is an OAM accelerator for qualified liquid-cooled systems. A bare module, incomplete baseboard or unsupported server is not a usable local-AI machine.
B300 vs MI355X specifications
| Characteristic | NVIDIA B300 | AMD Instinct MI355X |
|---|---|---|
| Architecture | Blackwell Ultra | CDNA 4 / gfx950 |
| Software ecosystem | CUDA | ROCm |
| Headline memory | 288GB HBM3E tier | 288GB HBM3E |
| Conservative per-device planning | 270GB in NVIDIA HGX B300 datasheet | 288GB published |
| Headline bandwidth | Up to 8TB/s; 7.7TB/s in HGX datasheet | 8TB/s |
| Scale-up interconnect | Fifth-generation NVLink, up to 1.8TB/s per GPU | 7 × 153.6GB/s Infinity Fabric links |
| Host interface | PCIe Gen 6, 256GB/s | PCIe 5.0 x16 |
| Maximum accelerator power | Up to 1100W for HGX B300 | 1400W |
| Cooling/platform | SXM in qualified HGX/DGX system; verify system design | OAM, direct liquid cooled |
| Eight-device aggregate headline | 2.3TB | About 2.3TB |
The capacity difference hidden inside “288GB”
NVIDIA’s enterprise architecture materials describe B300 as a 288GB class accelerator, while the Blackwell Ultra datasheet lists 270GB for an individual HGX B300 GPU. AMD lists MI355X at 288GB. For boundary planning, this guide uses 270GB for B300 and 288GB for MI355X. Confirm what the exact system exposes to the runtime before purchase.
Qwen3.5-122B: one-device file fit
| Artifact | Files | B300 margin at 270GB | MI355X margin at 288GB |
|---|---|---|---|
| GPTQ 4-bit | 78.864GB | 191.136GB | 209.136GB |
| Q4 plus projector | 77.446GB | 192.554GB | 210.554GB |
| Official FP8 | 127.163GB | 142.837GB | 160.837GB |
| Q8 plus projector | 130.781GB | 139.219GB | 157.219GB |
| Official BF16 | 250.173GB | 19.827GB: tight | 37.827GB: still benchmark |
Both hold the listed BF16 files, but neither row proves usable runtime headroom. Cache, context, concurrency, temporary buffers and multimodal processing consume memory beyond checkpoint files.
Qwen3.5-397B: one-device and multi-device boundaries
| Artifact | Files | B300 at 270GB | MI355X at 288GB |
|---|---|---|---|
| GPTQ 4-bit | 235.708GB | Fits by files; 34.292GB margin | Fits by files; 52.292GB margin |
| Q4 plus projector | 245.012GB | Fits by files; 24.988GB margin | Fits by files; 42.988GB margin |
| Official FP8 | 406.152GB | 2 devices arithmetic minimum | 2 devices arithmetic minimum |
| Q8 plus projector | 422.426GB | 2 devices arithmetic minimum | 2 devices arithmetic minimum |
| Official BF16 | 806.796GB | 3 × 270GB leaves 3.204GB; plan 4 | 3 × 288GB leaves 57.204GB; benchmark, with 4 safer |
Aggregate arithmetic is not automatic pooling. The framework must shard or place weights, experts, cache and activations across separate devices. For BF16, three B300s are a file-size trap. Three MI355X devices have more aggregate margin but still require measured peak-memory evidence.
CUDA or ROCm: which should decide the purchase?
| Question | Favor B300 when… | Favor MI355X when… |
|---|---|---|
| Exact runtime support | The required framework, quantization and multimodal kernels are validated on Blackwell Ultra/CUDA | They are validated on gfx950/ROCm |
| Existing infrastructure | Your deployment, operators and monitoring already target CUDA/NVLink | Your deployment is standardized on ROCm/Infinity Fabric |
| Capacity boundary | The measured workload fits the system’s exposed B300 memory | The workload benefits materially from the published 288GB per device |
| Power and cooling | The qualified HGX/DGX design fits the facility | A 1400W direct-liquid-cooled OAM platform fits the facility |
| Evidence | A representative benchmark wins on complete-system cost and throughput | A representative benchmark wins on complete-system cost and throughput |
Do not compare vendor tensor numbers directly
Peak figures can differ by datatype, sparsity, clock, qualification and counting convention. They do not establish tokens per second, latency, power efficiency or output quality for your model. Require the same checkpoint, precision, context, batch/concurrency, runtime settings and measurement method on both systems.
Purchase and benchmark checklist
- Get the exact server SKU, accelerator identity, exposed memory and supported software matrix.
- Confirm cooling, rack power, host CPUs, system memory, storage and networking.
- Record driver, CUDA or ROCm, framework, container, kernels, model revision and quantization.
- Record topology, per-device placement and all CPU-memory or storage offload.
- Measure peak device and system memory, time to first token, prompt processing, generation, throttling and wall power.
- Compare supported complete-system price, warranty and service—not loose-module prices.
Plan the next step
- Read the full B300 model-fit guide
- Read the MI350X/MI355X guide
- Compare CUDA and ROCm
- Build a reproducible benchmark record
- Review Qwen3.5-122B capacity tiers
- Review Qwen3.5-397B capacity tiers
- Compare the full large-model hardware map
- Browse the hardware guide
- Check the live hardware market
Primary sources
- NVIDIA HGX AI Factory component specifications
- NVIDIA Blackwell Ultra datasheet
- AMD ROCm MI350-series architecture reference
- AMD MI355X system-acceptance documentation
Capacity calculations use decimal GB. Specifications and software support can change; verify the exact qualified system before purchase.
Need the smaller cross-vendor tiers? Compare AMD MI325X 256GB with NVIDIA B200 180GB before moving to the 270GB–288GB B300/MI355X class.
Want the all-NVIDIA comparison first? Compare B200 and B300 for local AI before weighing B300 against AMD.
Need the AMD capacity ladder first? Use the MI300X/MI325X/MI350X/MI355X decision hub before comparing MI355X with B300.