NVIDIA96 GB VRAM1,792 GB/s$1.5/hr

Qwen3.8 27B (VLM) on RTX PRO 6000 Blackwell 96GB: Does It Fit, How Fast, What It Costs

Qwen3.8 27B (VLM) at FP8 (E4M3) needs 27.8 GB for its weights and 33.3 GB in total at 8K context with 8 concurrent users. That means it fits on a single RTX PRO 6000 Blackwell 96GB. Every figure is computed by tokcalc's open-source capacity model.

Fits?
Yes, 1 GPU
33.3 GB needed / 96 GB available
Decode speed
62.89
tok/s per stream
Aggregate
754.7
tok/s @ 8 concurrent
Cost / 1M tokens
$0.5521
1× RTX PRO 6000 Blackwell 96GB
What changes as you add RTX PRO 6000 Blackwell 96GBs
Tensor-parallel degree vs VRAM, throughput, and cost. Cost scales linearly with GPU count; decode throughput scales near-linearly too, so cost per token stays roughly flat.
GPUsTotal VRAMVRAM neededDecode tok/sAggregate tok/sTTFTCost / 1M tokFits?
1× RTX96 GB33.3 GB62.89754.77.33 s$0.5521✓
2× RTX192 GB33.3 GB106.925663.68 s$0.3248✓
4× RTX384 GB33.3 GB213.8102641.86 s$0.1624✓
8× RTX768 GB33.3 GB427.741056942.7 ms$0.0812✓
Concurrency vs context length
On 1× RTX PRO 6000 Blackwell 96GB at FP8 (E4M3). KV cache scales linearly with context, so this is the number of users your GPUs can actually serve.
ContextConcurrent usersKV per requestFits?
4K1621.07 GB✓ fits
8K992.15 GB✓ fits
33K298.59 GB✓ fits
66K1517.18 GB✓ fits
131K734.36 GB✓ fits
Other GPUs that run Qwen3.8 27B (VLM)
Same workload, same quantization — ranked by value (tokens per dollar-hour).
GPUGPUsAggregate tok/sCost / 1M tok
RTX 5090 32GB2×2566$0.1083
AMD MI300X 192GB1×2232$0.3111
A100 40GB SXM41×652.8$0.3830
AMD MI325X 288GB1×2527$0.3847
A100 80GB SXM41×842.3$0.3957
Intel Gaudi 3 128GB1×1390$0.3997
Frequently asked questions

Can Qwen3.8 27B (VLM) run on RTX PRO 6000 Blackwell 96GB?

Yes. Qwen3.8 27B (VLM) at FP8 (E4M3) needs 33.3 GB including the KV cache, and RTX PRO 6000 Blackwell 96GB has 96 GB — so it fits on a single GPU.

How fast is Qwen3.8 27B (VLM) on RTX PRO 6000 Blackwell 96GB?

62.89 tokens/sec per stream and 754.7 tok/s aggregate with continuous batching at 8 concurrent users. Time to first token is 7.33 s, and inter-token latency is 15.9 ms.

What does Qwen3.8 27B (VLM) on RTX PRO 6000 Blackwell 96GB cost per million tokens?

$0.5521 per 1M output tokens at 1× RTX PRO 6000 Blackwell 96GB ($1.50/hr). That is the marginal hardware cost — add idle capacity, ops, and egress for the real figure.

How much VRAM does Qwen3.8 27B (VLM) need on RTX PRO 6000 Blackwell 96GB?

27.8 GB for the weights at FP8 (E4M3), plus 5.5 GB of KV cache for 8 concurrent requests at 8K context — 33.3 GB total. Every extra concurrent user at long context costs more, so leave headroom.

Is RTX PRO 6000 Blackwell 96GB the right choice for Qwen3.8 27B (VLM)?

It works. Compare $0.5521 per 1M tokens against the alternatives below — the cheapest option that fits the same workload is 4× RTX 3080 10GB at $0.0511, and the fastest is 1× Cerebras CS-3 (WSE-3) at 8423 tok/s.

Adjust the workload yourself

Same formulas, your context length, concurrency, and quantisation.

Open in calculator