DeepSeek V4 Flash needs ~178.2 GB VRAM. NVIDIA B200 180GB has 180.0 GB. With NVFP4 quantization, expect ~145 tok/s.
Operating mode
Interactive favors responsiveness, while light API and scale-out lean harder on serving readiness. The fit stays the same, but the recommendation lens changes.
Current mode
Balanced
Balanced for general local use. Keeps the ranking neutral across personal and serving workflows.
Select quantization to explore
Fit status
Runs with offload
Decode
144.8 tok/s
TTFT
1337 ms
Safe context
38K
Memory
178.2 GB / 180.0 GB
This setup is broadly balanced for this model.
Very little memory headroom
You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | S | Runs with offload | 144.8 tok/s | 729 ms | 38K |
| Coding | S | Runs with offload | 144.8 tok/s | 1337 ms | 38K |
| Agentic Coding | S | Runs with offload | 144.8 tok/s | 1945 ms | 38K |
| Reasoning | S | Runs with offload | 144.8 tok/s | 1580 ms | 38K |
| RAG | S | Runs with offload | 144.8 tok/s | 2431 ms | 38K |
Inference speed
Estimated decode speed (tokens/sec) for DeepSeek V4 Flash at NVFP4 across popular GPUs and Apple Silicon, including multi-GPU rigs, using the fastest local runtime per device. Fastest is Mac Studio M3 Ultra 256GB at ~18 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.
| GPU / Mac | Memory | Quant | Speed (tok/s) | Fits? |
|---|---|---|---|---|
Mac Studio M3 Ultra 256GB | 256 GB | NVFP4 | 17.8 | Offloads |
Mac Studio M2 Ultra 128GB | 128 GB | NVFP4 | 7.0 | Too big |
Mac Studio M1 Ultra 128GB | 128 GB | NVFP4 | 6.7 | Too big |
MacBook Pro M4 Max 128GB | 128 GB | NVFP4 | 5.2 | Too big |
MacBook Pro M4 Max 64GB | 64 GB | NVFP4 | 5.2 | Too big |
 RTX 5090 32GB | 32 GB | NVFP4 | 3.9 | Too big |
2× RX 7900 XTX 24GB | 48 GB | NVFP4 | 3.8 | Too big |
MacBook Pro M3 Max 64GB | 64 GB | NVFP4 | 3.6 | Too big |
MacBook Pro M1 Max 64GB | 64 GB | NVFP4 | 3.3 | Too big |
MacBook Pro M4 Pro 48GB | 48 GB | NVFP4 | 3.2 | Too big |
| 2× RTX 4090 24GB | 48 GB | NVFP4 | 2.6 | Too big |
| RTX 4090 24GB | 24 GB | NVFP4 | 2.5 | Too big |
| 2× RTX 3090 24GB | 48 GB | NVFP4 | 2.3 | Too big |
RX 7900 XTX 24GB | 24 GB | NVFP4 | 2.2 | Too big |
| RTX 3090 24GB | 24 GB | NVFP4 | 2.1 | Too big |
| RTX 4080 Super 16GB | 16 GB | NVFP4 | 2.0 | Too big |
| RTX 4070 12GB | 12 GB | NVFP4 | 2.0 | Too big |
| RTX 3060 12GB | 12 GB | NVFP4 | 2.0 | Too big |
| RTX 4060 8GB | 8 GB | NVFP4 | 2.0 | Too big |
| 4× RTX 3060 12GB | 48 GB | NVFP4 | 2.0 | Too big |
Estimates for single-stream decoding at NVFP4; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.
How DeepSeek V4 Flash (284B params) fits at each quantization level on NVIDIA B200 180GB (180.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 110.8 GB | Low | S90 |
Q3_K_SBest for your GPU | 3 | 139.2 GB | Low | S90 |
NVFP4 | 4 | 159.0 GB | Medium | F0 |
Q4_K_M | 4 | 173.2 GB | Medium | F0 |
Q5_K_M | 5 | 204.5 GB | High | F0 |
Q6_K | 6 | 232.9 GB | High | F0 |
Q8_0 | 8 | 303.9 GB | Very High | F0 |
F16 | 16 | 582.2 GB | Maximum | F0 |
Copy-paste commands to run DeepSeek V4 Flash on your machine.
Run
docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
--hf-repo "deepseek-ai/DeepSeek-V4-Flash" \
--hf-file "DeepSeek-V4-Flash-NVFP4.gguf" \
-c 4096 -ngl 99Yes, NVIDIA B200 180GB can run DeepSeek V4 Flash with a S grade (Runs with offload). Expected decode speed: 144.8 tok/s.
DeepSeek V4 Flash (284B parameters) requires approximately 178.2 GB of memory with NVFP4 quantization.
The recommended quantization for DeepSeek V4 Flash is NVFP4, which balances quality and memory efficiency.
On NVIDIA B200 180GB, DeepSeek V4 Flash achieves approximately 144.8 tokens per second decode speed with a time-to-first-token of 1337ms using NVFP4 quantization.
For coding workloads, DeepSeek V4 Flash on NVIDIA B200 180GB receives a S grade with 144.8 tok/s and 38K context.
On NVIDIA B200 180GB, DeepSeek V4 Flash can safely use up to 38K tokens of context. The model's official context limit is 1.0M, but available memory constrains the safe maximum.
Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/deepseek-v4-flash-on-b200-180gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview: