Makes the model fit on the accelerator instead of staying completely out of reach.
Removes host-memory offload, which is usually the single biggest latency and throughput win.
~$8,000 MSRP
DeepSeek V4 Flash needs ~164.5 GB but AMD Instinct MI100 32GB only has 32.0 GB. Try a smaller quantization or lighter model.
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
131.4 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
2.6 tok/s
TTFT
75056 ms
Safe context
4K
Memory
163.4 GB / 32.0 GB
Offload
80%
Usable VRAM is the main blocker for this model.
Not enough usable memory
The model needs 164.5 GB, but this setup only exposes 32.0 GB of usable VRAM.
Add more VRAM headroom
The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | F | Too heavy | 2.6 tok/s | 40939 ms | 4K |
| Coding | F | Too heavy | 2.3 tok/s | 82444 ms | 4K |
| Agentic Coding | F | Too heavy | 2.6 tok/s | 109172 ms | 4K |
| Reasoning | F | Too heavy | 2.6 tok/s | 88702 ms | 4K |
| RAG | F | Too heavy | 2.6 tok/s | 136465 ms | 4K |
How DeepSeek V4 Flash (284B params) fits at each quantization level on AMD Instinct MI100 32GB (32.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 110.8 GB | Low | F0 |
Q3_K_S | 3 | 139.2 GB | Low | F0 |
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 |
Opções de upgrade
Makes the model fit on the accelerator instead of staying completely out of reach.
Removes host-memory offload, which is usually the single biggest latency and throughput win.
~$8,000 MSRP
Makes the model fit on the accelerator instead of staying completely out of reach.
Removes host-memory offload, which is usually the single biggest latency and throughput win.
~$15,000 MSRP
Makes the model fit on the accelerator instead of staying completely out of reach.
Removes host-memory offload, which is usually the single biggest latency and throughput win.
~$20,000 MSRP
No, DeepSeek V4 Flash requires more memory than AMD Instinct MI100 32GB provides.
DeepSeek V4 Flash (284B parameters) requires approximately 164.5 GB of memory with NVFP4 quantization.
The recommended quantization for DeepSeek V4 Flash is NVFP4, which balances quality and memory efficiency.
On AMD Instinct MI100 32GB, DeepSeek V4 Flash achieves approximately 2.3 tokens per second decode speed with a time-to-first-token of 82444ms using NVFP4 quantization.
For coding workloads, DeepSeek V4 Flash on AMD Instinct MI100 32GB receives a F grade with 2.3 tok/s and 4K context.
On AMD Instinct MI100 32GB, DeepSeek V4 Flash can safely use up to 4K tokens of context. The model's official context limit is 1.0M, but available memory constrains the safe maximum.
Add more VRAM headroom. The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/deepseek-v4-flash-on-instinct-mi100-32gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview: