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

Llama 3.1 405B on B300 SXM 288GB: Does It Fit, How Fast, What It Costs

Llama 3.1 405B at AWQ 4-bit needs 222.8 GB for its weights and 256.6 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
256.6 GB needed / 288 GB available
Decode speed
20.54
tok/s per stream
Aggregate
246.5
tok/s @ 8 concurrent
Cost / 1M tokens
$9.01
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 GB256.6 GB20.54246.58.93 s$9.01✓
2× B300576 GB256.6 GB34.92838.24.48 s$5.30✓
4× B3001152 GB256.6 GB69.8533532.26 s$2.65✓
8× B3002304 GB256.6 GB139.7134111.14 s$1.33✓
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?
4K302.11 GB✓ fits
8K154.23 GB✓ fits
33K316.91 GB✓ fits
66K133.82 GB✓ fits
131K067.65 GB✗ does not fit
Other GPUs that run Llama 3.1 405B
Same workload, same quantization — ranked by value (tokens per dollar-hour).
GPUGPUsAggregate tok/sCost / 1M tok
L40S 48GB8×1448$1.07
A100 80GB SXM44×838.2$1.59
H100 SXM5 80GB4×1404$1.98
B200 SXM 192GB2×838.2$3.98
H200 SXM5 141GB2×502.9$4.42
AMD MI325X 288GB1×184.9$5.26
Frequently asked questions

Can Llama 3.1 405B run on B300 SXM 288GB?

Yes. Llama 3.1 405B at AWQ 4-bit needs 256.6 GB including the KV cache, and B300 SXM 288GB has 288 GB — so it fits on a single GPU.

How fast is Llama 3.1 405B on B300 SXM 288GB?

20.54 tokens/sec per stream and 246.5 tok/s aggregate with continuous batching at 8 concurrent users. Time to first token is 8.93 s, and inter-token latency is 48.7 ms.

What does Llama 3.1 405B on B300 SXM 288GB cost per million tokens?

$9.01 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 Llama 3.1 405B need on B300 SXM 288GB?

222.8 GB for the weights at AWQ 4-bit, plus 33.8 GB of KV cache for 8 concurrent requests at 8K context — 256.6 GB total. Every extra concurrent user at long context costs more, so leave headroom.

Is B300 SXM 288GB the right choice for Llama 3.1 405B?

It works. Compare $9.01 per 1M tokens against the alternatives below — the cheapest option that fits the same workload is 8× A100 40GB SXM4 at $0.7697, and the fastest is 8× Cerebras CS-3 (WSE-3) at 33527 tok/s.

Adjust the workload yourself

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

Open in calculator