NVIDIA288 GB VRAM8,000 GB/s$8/hr

DeepSeek Coder V2 236B (MoE) on B300 SXM 288GB: 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 fits on a single B300 SXM 288GB. Every figure is computed by tokcalc's open-source capacity model.

Fits?
Yes, 1 GPU
142.4 GB needed / 288 GB available
Decode speed
396.2
tok/s per stream
Aggregate
4754
tok/s @ 8 concurrent
Cost / 1M tokens
$0.4674
1× B300 SXM 288GB
What changes as you add B300 SXM 288GBs
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× B300288 GB142.4 GB396.24754491.3 ms$0.4674✓
2× B300576 GB142.4 GB673.516165260.9 ms$0.2749✓
4× B3001152 GB142.4 GB134764658145.4 ms$0.1375✓
8× B3002304 GB142.4 GB269425863387.7 ms$0.0687✓
Concurrency vs context length
On 1× B300 SXM 288GB 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?
4K200807.5 MB✓ fits
8K1001.58 GB✓ fits
33K256.31 GB✓ fits
66K1212.62 GB✓ fits
131K625.23 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
A100 80GB SXM42×4041$0.1650
H100 SXM5 80GB2×6769$0.2052
AMD MI300X 192GB1×3150$0.2205
H200 SXM5 141GB2×9699$0.2291
Frequently asked questions

Can DeepSeek Coder V2 236B (MoE) run on B300 SXM 288GB?

Yes. DeepSeek Coder V2 236B (MoE) at AWQ 4-bit needs 142.4 GB including the KV cache, and B300 SXM 288GB has 288 GB — so it fits on a single GPU.

How fast is DeepSeek Coder V2 236B (MoE) on B300 SXM 288GB?

396.2 tokens/sec per stream and 4754 tok/s aggregate with continuous batching at 8 concurrent users. Time to first token is 491.3 ms, and inter-token latency is 2.5 ms.

What does DeepSeek Coder V2 236B (MoE) on B300 SXM 288GB cost per million tokens?

$0.4674 per 1M output tokens at 1× B300 SXM 288GB ($8.00/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 B300 SXM 288GB?

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 B300 SXM 288GB the right choice for DeepSeek Coder V2 236B (MoE)?

It works. Compare $0.4674 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