Short answer: NVIDIA B300 changes several model-fit boundaries that B200 cannot cross on one GPU. B200 provides 180GB HBM3E per accelerator; B300 is marketed as a 288GB tier, while NVIDIA’s Blackwell Ultra datasheet lists 270GB and 7.7TB/s for the individual HGX B300 GPU. For conservative purchasing math, this guide uses 270GB for B300. That is enough for Qwen3.5-122B BF16 files and Qwen3.5-397B 4-bit files on one device—both of which exceed one B200’s capacity.
Neither product is a desktop GPU. B200 and B300 are SXM-class datacenter accelerators sold in qualified HGX/DGX systems. Compare the complete server, usable memory, power and cooling, CUDA stack, topology, support and measured workload—not bare-module listings.
B200 vs B300 specifications
| Specification | NVIDIA B200 | NVIDIA B300 |
|---|---|---|
| Architecture | Blackwell | Blackwell Ultra |
| Memory per GPU | 180GB HBM3E | 288GB product tier; 270GB in NVIDIA’s individual HGX B300 datasheet |
| Published bandwidth | Up to 8TB/s | Up to 8TB/s tier; 7.7TB/s in the HGX B300 datasheet |
| Eight-GPU node memory | 1.44TB | 2.30TB marketed aggregate |
| NVLink generation | Fifth generation | Fifth generation |
| GPU-to-GPU bandwidth | Up to 1.8TB/s per GPU | Up to 1.8TB/s per GPU |
| Aggregate NVLink bandwidth | 14.4TB/s | 14.4TB/s |
| Maximum configurable power | Up to 1,000W | Up to 1,100W |
What fits: Qwen3.5-122B-A10B
| Artifact | Files | One B200 (180GB) | One B300 (270GB planning) |
|---|---|---|---|
| GPTQ 4-bit | 78.864GB | Fits; 101.136GB file margin | Fits; 191.136GB margin |
| Q4 plus projector | 77.446GB | Fits; 102.554GB margin | Fits; 192.554GB margin |
| Official FP8 | 127.163GB | Fits; 52.837GB margin | Fits; 142.837GB margin |
| Q8 plus projector | 130.781GB | Fits; 49.219GB margin | Fits; 139.219GB margin |
| Official BF16 | 250.173GB | No; arithmetic minimum 2 × 180GB | Fits by files; only 19.827GB margin |
B300’s headline win is the BF16 boundary, but 19.827GB is a tight remainder for CUDA context, KV cache, temporary buffers, multimodal processing and concurrency. Treat it as a test candidate, not a deployment guarantee.
What fits: Qwen3.5-397B-A17B
| Artifact | Files | B200 minimum by files | B300 minimum by files |
|---|---|---|---|
| GPTQ 4-bit | 235.708GB | 2 × 180GB | 1 × 270GB; 34.292GB margin |
| Q4 plus projector | 245.012GB | 2 × 180GB | 1 × 270GB; 24.988GB margin |
| Official FP8 | 406.152GB | 3 × 180GB = 540GB | 2 × 270GB = 540GB |
| Q8 plus projector | 422.426GB | 3 × 180GB = 540GB | 2 × 270GB = 540GB |
| Official BF16 | 806.796GB | 5 × 180GB = 900GB | 3 × 270GB = 810GB is only 3.204GB over the files; plan 4 |
The 4-bit rows are the clearest B300 advantage: a 235–245GB artifact can fit on one conservatively planned B300 but not one B200. Multi-GPU totals are arithmetic only. Device memories remain separate, and the runtime must shard or parallelize the model.
When B300 changes the outcome
- Single-device 250GB-class work: B300 can cross a boundary that B200 cannot, although runtime headroom may still be tight.
- Single-device 397B 4-bit artifacts: B300 can eliminate the minimum two-device requirement imposed by 180GB.
- Large multi-GPU artifacts: B300 may reduce the arithmetic device count, but a nearly exact fit is not a credible operating plan.
- Workloads already comfortable in 180GB: capacity alone may not justify B300. Compare measured throughput, availability, platform cost and support.
Software compatibility needs a fresh validation
NVIDIA lists B200 as Blackwell GB100 with compute capability 10.0. B300 is Blackwell Ultra. Do not assume that a container, framework build, quantization kernel or compiled extension validated on B200 automatically works correctly on the exact B300 system. Confirm the current driver, CUDA toolkit, framework, serving runtime, communication libraries and model-specific kernels with the vendor or run a representative acceptance test.
Benchmark before procurement
- Record the exact system SKU and the memory CUDA exposes per accelerator.
- Pin the driver, CUDA, container, framework, kernels and model revision.
- Record model format, context, batch, concurrency, per-device placement and offload.
- Measure peak device memory, time to first token, prompt throughput, generation throughput, throttling and wall power.
- Compare complete supported systems, warranty and delivery—not isolated accelerator claims.
Plan the next step
- Review the B300 capacity guide
- Compare H200 and B200
- Compare B300 and AMD MI355X
- Build a reproducible benchmark record
- See Qwen3.5-122B capacity tiers
- See Qwen3.5-397B capacity tiers
- Compare all large-model hardware paths
- Browse the hardware guide
- Follow the high-VRAM watchlist
Primary sources
- NVIDIA HGX AI Factory components: H200, B200 and B300
- NVIDIA HGX H100/H200/B200 enterprise-reference components
- NVIDIA DGX B200 specifications
- NVIDIA MIG supported-GPU table
- NVIDIA Blackwell Ultra datasheet
Capacity calculations use decimal GB and compare artifact files with nominal device capacity. They are not runtime-memory guarantees. Product specifications and software support can change; verify the exact system before purchase.
Need the B200 details first? Review the standalone NVIDIA B200 180GB guide before comparing it with B300.
See the Hopper starting points too: compare H100, H200, B200 and B300 across the complete datacenter ladder.