NVIDIA32 GB VRAM1,792 GB/s$0.5/hr

DeepSeek Coder V2 236B (MoE) on RTX 5090 32GB: Does It Fit, How Fast, What It Costs

DeepSeek Coder V2 236B (MoE) at AWQ 4-bit needs 129.8 GB for its weights and 142.4 GB in total at 8K context with 8 concurrent users. That means it needs 8× RTX 5090 32GB. Every figure is computed by tokcalc's open-source capacity model.

Fits?
Yes, 8×
142.4 GB needed / 256 GB available
Decode speed
603.5
tok/s per stream
Aggregate
57934
tok/s @ 8 concurrent
Cost / 1M tokens
$0.0192
8× RTX 5090 32GB

RTX 5090 32GB has no NVLink, so 8-way tensor parallelism runs over PCIe. tokcalc applies a flat 0.85× multi-GPU efficiency factor for this, which is realistic for NVLink-equipped datacenter cards but optimistic for PCIe scale-out — treat the throughput and cost figures for this configuration as an upper bound, and measure before committing.

What changes as you add RTX 5090 32GBs
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× RTX32 GB142.4 GB0.0000.000——✗
2× RTX64 GB142.4 GB0.0000.000——✗
4× RTX128 GB142.4 GB0.0000.000——✗
8× RTX256 GB142.4 GB603.557934850.9 ms$0.0192✓
Concurrency vs context length
On 8× RTX 5090 32GB 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?
4K160807.5 MB✓ fits
8K801.58 GB✓ fits
33K206.31 GB✓ fits
66K1012.62 GB✓ fits
131K525.23 GB✓ fits
Other GPUs that run DeepSeek Coder V2 236B (MoE)
Same workload, same quantization — ranked by value (tokens per dollar-hour).
GPUGPUsAggregate tok/sCost / 1M tok
L40S 48GB4×6983$0.1114
A100 80GB SXM42×4041$0.1650
H100 SXM5 80GB2×6769$0.2052
AMD MI300X 192GB1×3150$0.2205
H200 SXM5 141GB2×9699$0.2291
AMD MI325X 288GB1×3566$0.2727
Frequently asked questions

Can DeepSeek Coder V2 236B (MoE) run on RTX 5090 32GB?

Yes, but it needs 8× RTX 5090 32GB. At AWQ 4-bit the total footprint is 142.4 GB (129.8 GB of weights plus 12.6 GB of KV cache at 8K × 8 concurrent users), against 256 GB of available VRAM across 8 GPUs.

How fast is DeepSeek Coder V2 236B (MoE) on RTX 5090 32GB?

603.5 tokens/sec per stream and 57934 tok/s aggregate with continuous batching at 8 concurrent users. Time to first token is 850.9 ms, and inter-token latency is 1.7 ms.

What does DeepSeek Coder V2 236B (MoE) on RTX 5090 32GB cost per million tokens?

$0.0192 per 1M output tokens at 8× RTX 5090 32GB ($0.5000/hr). That is the marginal hardware cost — add idle capacity, ops, and egress for the real figure.

How much VRAM does DeepSeek Coder V2 236B (MoE) need on RTX 5090 32GB?

129.8 GB for the weights at AWQ 4-bit, plus 12.6 GB of KV cache for 8 concurrent requests at 8K context — 142.4 GB total. Every extra concurrent user at long context costs more, so leave headroom.

Is RTX 5090 32GB the right choice for DeepSeek Coder V2 236B (MoE)?

It works. Compare $0.0192 per 1M tokens against the alternatives below — the cheapest option that fits the same workload is 8× RTX 5090 32GB at $0.0192, and the fastest is 4× Cerebras CS-3 (WSE-3) at 161646 tok/s.

Adjust the workload yourself

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

Open in calculator