NVIDIA80 GB VRAM2,000 GB/s$1.2/hr

DeepSeek Coder V2 236B (MoE) on A100 80GB SXM4: 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 2× A100 80GB SXM4. Every figure is computed by tokcalc's open-source capacity model.

Fits?
Yes, 2×
142.4 GB needed / 160 GB available
Decode speed
168.4
tok/s per stream
Aggregate
4041
tok/s @ 8 concurrent
Cost / 1M tokens
$0.1650
2× A100 80GB SXM4
What changes as you add A100 80GB SXM4s
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× A10080 GB142.4 GB0.0000.000——✗
2× A100160 GB142.4 GB168.440411.14 s$0.1650✓
4× A100320 GB142.4 GB336.816165584.4 ms$0.0825✓
8× A100640 GB142.4 GB673.564658307.2 ms$0.0412✓
Concurrency vs context length
On 2× A100 80GB SXM4 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?
4K38807.5 MB✓ fits
8K191.58 GB✓ fits
33K46.31 GB✓ fits
66K212.62 GB✓ fits
131K125.23 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
RTX 5090 32GB8×57934$0.0192
L40S 48GB4×6983$0.1114
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 A100 80GB SXM4?

Yes, but it needs 2× A100 80GB SXM4. 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 160 GB of available VRAM across 2 GPUs.

How fast is DeepSeek Coder V2 236B (MoE) on A100 80GB SXM4?

168.4 tokens/sec per stream and 4041 tok/s aggregate with continuous batching at 8 concurrent users. Time to first token is 1.14 s, and inter-token latency is 5.9 ms.

What does DeepSeek Coder V2 236B (MoE) on A100 80GB SXM4 cost per million tokens?

$0.1650 per 1M output tokens at 2× A100 80GB SXM4 ($1.20/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 A100 80GB SXM4?

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 A100 80GB SXM4 the right choice for DeepSeek Coder V2 236B (MoE)?

It works. Compare $0.1650 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