Data CenterBlackwellNVLINKCUDA
Operating mode
Choose the operating mode for this hardware
Use this to bias workload recommendations toward responsiveness, background autonomy, lighter serving, or multi-GPU scale-out.
Current mode
Balanced
Balanced for general local use. Keeps the ranking neutral across personal and serving workflows.
About this GPU for AI
The NVIDIA B100 is a Blackwell datacenter GPU designed as a drop-in upgrade for existing HGX H100 infrastructure, targeting 192 GB of HBM3e at 8,000 GB/s bandwidth and 1,750 TFLOPS FP16. As a lower-power Blackwell variant at 700W, it fits within the same thermal envelope as existing H100 SXM racks while delivering substantially more VRAM and higher compute. Note: as of early 2025, NVIDIA has reportedly scaled back B100 production in favor of B200 and GB200 allocations, making availability limited. If it ships, it would be the most VRAM-accessible Blackwell GPU at the 8-GPU HGX level.
Beyond LLMs
AI Capability Matrix
What AI tasks this GPU can handle — from text generation to image and video creation.
| Capability | Status | Representative Model | Detail |
|---|
| LLM Chat (7B) | Runs natively | Llama 3.1 8B Q4 | — |
| LLM Coding (30B) | Runs natively | Qwen 3 30B Q4 | — |
| LLM Large (70B) |
hbm-memorymassive-vramblackwell-architecturedatacenter-gradehigh-bandwidth
Specifications
Compute
FP161750 TFLOPS
INT83500 TOPS
ArchitectureBlackwell
Memory
VRAM192 GB
Bandwidth8000 GB/s
General
FamilyData Center
SegmentData Center
InterconnectNVLINK
Compute PlatformCUDA
MSRP$35,000
Key Features
192 GB HBM3e per card — 8,000 GB/s bandwidth1,750 TFLOPS FP16 / 3,500 INT8 TOPS with FP4 Tensor Core support700W TDP — designed as drop-in replacement for H100 SXM racksNVLink 5.0 with 1.8 TB/s per-GPU bandwidth2nd-gen Transformer Engine with FP4/FP8 supportHGX-compatible baseboard (plug-in H100 upgrade)
For AI Workloads
Strengths
- 192 GB HBM3e fits 70B models at FP16 with ample KV cache, or small-batched 405B models with Q4
- Drop-in H100 SXM infrastructure compatibility — upgrade existing systems without new racks
- 4x estimated inference speedup vs. H100 due to doubled silicon area and FP4 support
- 8,000 GB/s bandwidth enables very fast token generation for large models
Considerations
- Availability uncertain — NVIDIA reportedly deprioritized B100 in favor of B200/GB200; limited supply
- 700W TDP still requires robust cooling, despite being lower than B200
- FP4 software ecosystem still maturing — framework support for FP4 inference only recently landed
- Surpassed on performance-per-dollar by B200 for new deployments if cooling infrastructure allows
Blackwell is NVIDIA's fifth-generation RTX architecture, built on TSMC's 4NP process. It introduces 5th-generation Tensor Cores with native FP4 precision support, enabling double the inference throughput per watt compared to Ada Lovelace's FP8 operations. Key innovations include the Neural Rendering Pipeline for AI-driven shading and the debut of GDDR7 memory in consumer GPUs.
AI Relevance
FP4 Tensor Cores deliver the highest tokens-per-watt efficiency in any consumer architecture. Native FP4 quantization means models can run at lower precision with minimal quality loss, effectively doubling the effective VRAM for model weights.
Process: TSMC 4NPPlatform: CUDATensor Cores: Gen 5Precisions: FP32, FP16, BF16, FP8, FP4, INT8, INT4
Recommendations by Workload
Qwen 3.5 122B A10B matches Chat and keeps a practical fit profile. It is a recent-generation family, which helps on current local SOTA workloads. It fits natively with comfortable headroom. Context coverage stays within the requested workload envelope. Known distribution channels: huggingface, lm-studio.
Decode 169.4 tok/s · 131K ctx · llama.cppEST.
Devstral 2 123B Instruct is a specialized fit for Coding. It is a recent-generation family, which helps on current local SOTA workloads. It fits natively with comfortable headroom. Context coverage stays within the requested workload envelope. Known distribution channels: huggingface, lm-studio.
Decode 76.1 tok/s · 212K ctx · llama.cppEST.
Just out of reach
Models you could run with an upgrade
High-quality models that need a bit more memory
397BTier 100Needs ~263.7 GB
Also runs on 2× your GPU via NVLink — 169 tok/s
1000BTier 100Needs ~633.8 GB
Also runs on 4× your GPU via NVLink — 135 tok/s
1000BTier 100Needs ~633.8 GB
Also runs on 4× your GPU via NVLink — 135 tok/s
1600BTier 100Needs ~883.0 GB
Also runs on 8× your GPU via NVLink — 199 tok/s
754BTier 92Needs ~489.6 GB
Also runs on 4× your GPU via NVLink — 159 tok/s
Image & Video Generation
Diffusion Model Compatibility
52 of 52 models can generate images or video on your B100 192GB
Multi-GPU scaling
B100 192GB — Up to 8× via NVLink
Scale out with multiple GPUs for larger models. NVLink provides 1800 GB/s inter-GPU bandwidth with 7% overhead.
| Config | Effective memory | Models that fit | Est. bandwidth |
|---|
| 1× B100 | 192 GB | 359/374 | 8,000 GB/s |
| 2× B100 | 384 GB | 366/374 | 14,880 GB/s |
| 4× B100 | 768 GB | 373/374 | 29,760 GB/s |
| 8× B100 | 1536 GB | 374/374 | 59,520 GB/s |
Model counts use default quantization at coding workload settings. Multi-GPU scaling factor: 0.93× per additional GPU.
Upgrade paths
Upgrade from B100 192GB
See what you unlock with more powerful hardware
Upgrade options
Upgrade options
Frequently Asked Questions
8k
GB/s
B100 192GBCategory AvgAMD Instinct MI325X 256GB
| Image Gen (SDXL) | Runs natively | SDXL 1.0 FP16 | ~200ms per image |
| Image Gen (Flux) | Runs natively | Flux.1 Dev FP16 | ~800ms per image |
| Image Gen (SD 3.5) | Runs natively | SD 3.5 Large FP16 | ~900ms per image |
| Video Short (25f) | Runs natively | LTX Video 2B | ~100ms/frame |
| Video Long (100f) | Runs natively | Wan Video 14B | ~400ms/frame |
S
Devstral 2 123B Instruct is a specialized fit for Agentic Coding. It is a recent-generation family, which helps on current local SOTA workloads. It fits natively with comfortable headroom. Context coverage stays within the requested workload envelope. Known distribution channels: huggingface, lm-studio.
Decode 76.1 tok/s · 212K ctx · llama.cppEST.
Devstral 2 123B Instruct matches Reasoning and keeps a practical fit profile. It is a recent-generation family, which helps on current local SOTA workloads. It fits natively with comfortable headroom. Context coverage stays within the requested workload envelope. Known distribution channels: huggingface, lm-studio.
Decode 76.1 tok/s · 212K ctx · llama.cppEST.
Qwen 3.5 122B A10B matches RAG and keeps a practical fit profile. It is a recent-generation family, which helps on current local SOTA workloads. It fits natively with comfortable headroom. Context coverage stays within the requested workload envelope. Known distribution channels: huggingface, lm-studio.
Decode 169.4 tok/s · 131K ctx · llama.cppEST.
122B97.0 GB270 tok/s131K ctx
Image
| MAGI-1Video | 1280×720 | 400ms/frame | S |
Image models estimated at 1024×1024 (28 steps, FP16). Video models estimated at 768×512 (25 frames, 30 steps, FP16). Actual performance varies with runtime and system load.
There are 3 upgrade path(s) from B100 192GB: B100 192GB, AMD Instinct MI325X 256GB. Upgrading would unlock larger models and faster inference speeds.
Buying advice
Should you buy B100 192GB for local AI?
Excellent choice for local AI
Runs 40 of 50 top models well — a strong all-rounder for local inference.
What will limit you first
This setup is broadly balanced for this model.
No major red flags
This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.
Best upgrade itinerary
Unlocks 4 additional models that do not fit on the current setup.
Want more headroom? AMD Instinct MI325X 256GB (256.0 GB VRAM) is the next step up.
