Can the NVIDIA RTX PRO 6000 Blackwell run DeepSeek V4 Flash?
DeepSeek V4-Flash is a mixture-of-experts model with 284B total parameters and 13B activated per token (official README table; MIT). Config (deepseek-ai/DeepSeek-V4-Flash@60d8d70770c6776ff598c94bb586a859a38244f1) is model_type=deepseek_v4, 43 layers, hidden_size=4096, 64 attention heads, 1 KV head, head_dim=512, 256 routed experts × 6 per token + 1 shared, native context 1,048,576, sliding_window=128, YaRN rope_scaling factor 16. Shipped precision is FP4+FP8 mixed (experts FP4, most other params FP8). Resident Q4_K_M weights need about 187 GB (T08 documented calculation from 284B total — all experts must sit in memory; do not use the 13B active count as VRAM). No consumer GPU holds it. A 256 GB Mac Studio has 256 GB unified_memory_max; macOS GPU usable share is about 75% (192 GB, T13), so Q4_K_M is a tight/over-budget fit on usable-not-raw memory. Measured tok/s: not published here.
At Q4_K_M quantization with 4,096 context, the DeepSeek V4 Flash requires approximately 187.8GB of VRAM. The NVIDIA RTX PRO 6000 Blackwell has 96GB available (91.8GB shortfall).
VRAM Breakdown
| Component | Size (GB) | Source |
|---|---|---|
| Model weights (Q4_K_M) | 170.4 | calculated |
| Loading overhead (10%) | 17.04 | calculated |
| KV cache @ 4,096 context | 0.36 | calculated |
| Total required | 187.8 | |
| GPU memory available | 96 | vendor_spec |
| Headroom / shortfall | -91.8 |
Quantization Options
| Quant | Weights (GB) | Total @ 4K (GB) | Total @ 32K (GB) | Fits NVIDIA RTX PRO 6000 Blackwell? |
|---|---|---|---|---|
| Q4_K_M | 170.4 | 187.8 | 190.33 | No |
| Q5_K_M | 202.35 | 222.95 | 225.48 | No |
| Q6_K | 234.3 | 258.09 | 260.62 | No |
| Q8_0 | 301.75 | 332.29 | 334.82 | No |
| FP16 | 568 | 625.16 | 627.69 | No |
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If It Doesn't Fit: Alternatives
Same GPU, Other Models
Frequently Asked Questions
Can the DeepSeek V4 Flash run on 96GB VRAM?
No. The DeepSeek V4 Flash requires approximately 187.8GB at Q4_K_M quantization with 4K context. 96GB is not enough.
What is the maximum context length I can use on the NVIDIA RTX PRO 6000 Blackwell?
The DeepSeek V4 Flash supports up to 1,048,576 tokens of context. Larger context increases KV-cache memory requirements. At 4K context the model needs ~187.8GB; at 32K it needs more.
Which quantization should I use with the NVIDIA RTX PRO 6000 Blackwell?
Q4_K_M is the recommended starting point — it offers the best speed-to-quality tradeoff. If you have VRAM headroom, Q6_K or Q8_0 improves quality at the cost of speed. FP16 is only for inference servers with ample memory.
Can you run DeepSeek V4-Flash locally?
Only on data-center or large unified-memory hardware. Resident Q4_K_M weights need about 187 GB (calculated from the official 284B total). That is a single 192 GB NVIDIA B200 or at least four 80 GB GPUs. No consumer graphics card can run it without heavy offloading.
Why is VRAM so large if only 13B parameters activate per token?
DeepSeek V4-Flash is a Mixture-of-Experts model: all 284 billion parameters must sit in memory even though only 13 billion are active per token, according to the official Hugging Face README. VRAM use follows total resident parameters; generation speed follows activated parameters. Measured tok/s are not published here.
Related Pages
Full DeepSeek V4 Flash fit page · All AI models · NVIDIA RTX PRO 6000 Blackwell specs · VRAM calculator
VRAM figures are calculated from model architecture parameters using established formulas. Benchmark data is measured and cited per row. GPU memory is vendor_spec from manufacturer datasheets.