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

GPT-OSS 120B (MoE) on RTX PRO 6000 Blackwell 96GB: Does It Fit, How Fast, What It Costs

GPT-OSS 120B (MoE) at AWQ 4-bit needs 64.3 GB for its weights and 66.7 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
66.7 GB needed / 96 GB available
Decode speed
365.4
tok/s per stream
Aggregate
4385
tok/s @ 8 concurrent
Cost / 1M tokens
$0.0950
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 GB66.7 GB365.443851.37 s$0.0950✓
2× RTX192 GB66.7 GB621.214909700.1 ms$0.0559✓
4× RTX384 GB66.7 GB124259638365.0 ms$0.0279✓
8× RTX768 GB66.7 GB2485238551197.5 ms$0.0140✓
Concurrency vs context length
On 1× RTX PRO 6000 Blackwell 96GB at AWQ 4-bit. KV cache scales linearly with context, so this is the number of users your GPUs can actually serve.
ContextConcurrent usersKV per requestFits?
4K203309.2 MB✓ fits
8K103618.5 MB✓ fits
33K262.42 GB✓ fits
66K134.83 GB✓ fits
131K69.66 GB✓ fits
Other GPUs that run GPT-OSS 120B (MoE)
Same workload, same quantization — ranked by value (tokens per dollar-hour).
GPUGPUsAggregate tok/sCost / 1M tok
RTX 5090 32GB4×59638$0.009316
AMD MI300X 192GB1×12969$0.0535
L40S 48GB2×7188$0.0541
AMD MI325X 288GB1×14682$0.0662
A100 80GB SXM41×4894$0.0681
Intel Gaudi 3 128GB1×8075$0.0688
Frequently asked questions

Can GPT-OSS 120B (MoE) run on RTX PRO 6000 Blackwell 96GB?

Yes. GPT-OSS 120B (MoE) at AWQ 4-bit needs 66.7 GB including the KV cache, and RTX PRO 6000 Blackwell 96GB has 96 GB — so it fits on a single GPU.

How fast is GPT-OSS 120B (MoE) on RTX PRO 6000 Blackwell 96GB?

365.4 tokens/sec per stream and 4385 tok/s aggregate with continuous batching at 8 concurrent users. Time to first token is 1.37 s, and inter-token latency is 2.7 ms.

What does GPT-OSS 120B (MoE) on RTX PRO 6000 Blackwell 96GB cost per million tokens?

$0.0950 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 GPT-OSS 120B (MoE) need on RTX PRO 6000 Blackwell 96GB?

64.3 GB for the weights at AWQ 4-bit, plus 2.5 GB of KV cache for 8 concurrent requests at 8K context — 66.7 GB total. Every extra concurrent user at long context costs more, so leave headroom.

Is RTX PRO 6000 Blackwell 96GB the right choice for GPT-OSS 120B (MoE)?

It works. Compare $0.0950 per 1M tokens against the alternatives below — the cheapest option that fits the same workload is 8× RTX 3080 10GB at $0.004393, and the fastest is 2× Cerebras CS-3 (WSE-3) at 166400 tok/s.

Adjust the workload yourself

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

Open in calculator