Intel128 GB VRAM3,300 GB/s$2/hr

Pixtral 12B (VLM) on Intel Gaudi 3 128GB: Does It Fit, How Fast, What It Costs

Pixtral 12B (VLM) at FP16 needs 24.0 GB for its weights and 34.7 GB in total at 8K context with 8 concurrent users. That means it fits on a single Intel Gaudi 3 128GB. Every figure is computed by tokcalc's open-source capacity model.

Fits?
Yes, 1 GPU
34.7 GB needed / 128 GB available
Decode speed
89.38
tok/s per stream
Aggregate
1073
tok/s @ 8 concurrent
Cost / 1M tokens
$0.5180
1× Intel Gaudi 3 128GB
What changes as you add Intel Gaudi 3 128GBs
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× Intel128 GB34.7 GB89.381073255.5 ms$0.5180✓
2× Intel256 GB34.7 GB151.93647143.7 ms$0.3047✓
4× Intel512 GB34.7 GB303.91458686.9 ms$0.1524✓
8× Intel1024 GB34.7 GB607.85834458.4 ms$0.0762✓
Concurrency vs context length
On 1× Intel Gaudi 3 128GB at FP16. KV cache scales linearly with context, so this is the number of users your GPUs can actually serve.
ContextConcurrent usersKV per requestFits?
4K154687.2 MB✓ fits
8K771.34 GB✓ fits
33K195.37 GB✓ fits
66K910.74 GB✓ fits
131K421.47 GB✓ fits
Other GPUs that run Pixtral 12B (VLM)
Same workload, same quantization — ranked by value (tokens per dollar-hour).
GPUGPUsAggregate tok/sCost / 1M tok
RTX 5090 32GB2×1980$0.1403
AMD MI300X 192GB1×1723$0.4032
A100 40GB SXM41×503.8$0.4963
AMD MI325X 288GB1×1950$0.4986
A100 80GB SXM41×650.0$0.5128
H100 SXM5 80GB1×1089$0.6378
Frequently asked questions

Can Pixtral 12B (VLM) run on Intel Gaudi 3 128GB?

Yes. Pixtral 12B (VLM) at FP16 needs 34.7 GB including the KV cache, and Intel Gaudi 3 128GB has 128 GB — so it fits on a single GPU.

How fast is Pixtral 12B (VLM) on Intel Gaudi 3 128GB?

89.38 tokens/sec per stream and 1073 tok/s aggregate with continuous batching at 8 concurrent users. Time to first token is 255.5 ms, and inter-token latency is 11.2 ms.

What does Pixtral 12B (VLM) on Intel Gaudi 3 128GB cost per million tokens?

$0.5180 per 1M output tokens at 1× Intel Gaudi 3 128GB ($2.00/hr). That is the marginal hardware cost — add idle capacity, ops, and egress for the real figure.

How much VRAM does Pixtral 12B (VLM) need on Intel Gaudi 3 128GB?

24.0 GB for the weights at FP16, plus 10.7 GB of KV cache for 8 concurrent requests at 8K context — 34.7 GB total. Every extra concurrent user at long context costs more, so leave headroom.

Is Intel Gaudi 3 128GB the right choice for Pixtral 12B (VLM)?

It works. Compare $0.5180 per 1M tokens against the alternatives below — the cheapest option that fits the same workload is 4× RTX 3080 10GB at $0.0662, and the fastest is 1× Cerebras CS-3 (WSE-3) at 6500 tok/s.

Adjust the workload yourself

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

Open in calculator