Vision-languageDense7B params128K max context

Qwen 2.5-VL 7B (VLM) VRAM Requirements: How Many GPUs & What It Costs

Qwen 2.5-VL 7B (VLM) needs 14.0 GB of VRAM for its weights at FP16 — 14.2 GB once a KV cache is loaded. Below is the full breakdown: which GPUs fit, how many you need, what it serves, and what it costs per million tokens. Every figure comes from tokcalc's open-source capacity model.

Weights (FP16)
14.0 GB
14.2 GB with KV cache
Fits on 1 GPU?
Yes
A10G 24GB or larger
Cheapest config
$0.0772
2× RTX 3080 10GB / 1M tokens
Fastest config
11143
tok/s on 1× Cerebras CS-3 (WSE-3)
VRAM requirement by quantization
Weight footprint = params × bytes per param. The “with KV cache” column adds one request at 4K context.
QuantizationWeightsWith KV cacheSmallest single GPU
FP16recommended14.0 GB14.2 GBT4 16GB (16 GB)
FP8 (E4M3)7.0 GB7.2 GBRTX 3080 10GB (10 GB)
AWQ 4-bit3.9 GB4.1 GBRTX 3080 10GB (10 GB)
Which GPU should you use?
Reference workload: 8K context, 8 concurrent users, continuous batching, FP16. Ranked by fitting first, then by tokens per dollar-hour.
GPUGPUsTotal VRAMDecode tok/sAggregate tok/sCost / 1M tokens
RTX 3080 10GB2×20 GB59.991440$0.0772
RTX 4080 16GB2×32 GB56.591358$0.1227
RTX 3090 24GB1×24 GB43.46521.5$0.1332
RTX 5090 32GB1×32 GB83.20998.4$0.1391
T4 16GB2×32 GB25.26606.2$0.1833
RTX A4000 16GB2×32 GB35.36848.6$0.1964
RTX 4090 24GB1×24 GB46.80561.6$0.1978
AMD MI300X 192GB1×192 GB246.12953$0.2352
V100 SXM2 32GB1×32 GB41.79501.4$0.2770
A100 40GB SXM41×40 GB71.96863.6$0.2895
AMD MI325X 288GB1×288 GB278.63343$0.2908
A100 80GB SXM41×80 GB92.861114$0.2991

RTX 3080 10GB has no NVLink, so 2-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.

How concurrency changes with context length
On 2× RTX 3080 10GB at FP16. KV cache scales linearly with context, so long contexts cut the number of users you can serve from the same GPUs.
ContextConcurrent usersKV per requestFits?
4K25240.5 MB✓ fits
8K12481.0 MB✓ fits
33K31.88 GB✓ fits
66K13.76 GB✓ fits
131K07.52 GB✗ does not fit
Frequently asked questions

Can Qwen 2.5-VL 7B (VLM) run on a single GPU?

Yes. At FP16 the model needs 14.2 GB of VRAM, so it fits on a single A10G 24GB or anything with more VRAM. At full FP16 it needs 14.2 GB, which fits on one T4 16GB or larger.

How many GPUs does Qwen 2.5-VL 7B (VLM) need?

At FP16 with 8,192-token context and 8 concurrent users, the cheapest configuration that fits is 2× RTX 3080 10GB (20 GB total VRAM). $0.0772 per 1M output tokens.

How fast can Qwen 2.5-VL 7B (VLM) be served?

On 1× Cerebras CS-3 (WSE-3) at FP16, the aggregate throughput is 11143 tokens/sec with continuous batching. That hardware has no published rental price, so its cost per million tokens is not comparable.

How much VRAM does Qwen 2.5-VL 7B (VLM) need?

Weights alone: 14.0 GB at FP16, 14.0 GB at FP16. Add KV cache for your context length and concurrency — every concurrent request at 8,192 tokens costs extra VRAM, so budget headroom above the weight figure.

Is it cheaper to self-host Qwen 2.5-VL 7B (VLM) than to call an API?

At 2× RTX 3080 10GB the marginal cost is $0.0772 per 1M output tokens, before you add idle capacity, ops staff, or egress. That is typically cheaper than frontier API pricing at sustained volume, but API calls win when your load is bursty or too small to keep a GPU busy. The Build vs Buy tab on tokcalc prices this against a specific provider for you.

Try Qwen 2.5-VL 7B (VLM) with your own context and concurrency

The calculator uses the exact same formulas as this page — change the workload and watch every number update.

Open in calculator

Qwen 2.5-VL 7B (VLM) on specific GPUs

Qwen 2.5-VL 7B (VLM) on RTX 3090 24GBQwen 2.5-VL 7B (VLM) on RTX 5090 32GBQwen 2.5-VL 7B (VLM) on RTX 4090 24GBQwen 2.5-VL 7B (VLM) on AMD MI300X 192GBQwen 2.5-VL 7B (VLM) on V100 SXM2 32GBQwen 2.5-VL 7B (VLM) on A100 40GB SXM4Qwen 2.5-VL 7B (VLM) on AMD MI325X 288GBQwen 2.5-VL 7B (VLM) on A100 80GB SXM4
Model architecture
The numbers behind the KV-cache and throughput math.
Total parameters7.00B
Active parameters per token7.00B
ArchitectureDense
Attention layoutFull attention (GQA)
Transformer layers28
Hidden dimension3584
Attention heads28 query / 4 KV (GQA)
Head dimension128
Vocabulary size152,064
Max context (catalog)128K
KV cache per token @ 8K56.0 KB

Every layer caches keys and values for the whole context, so the KV cache grows linearly with conversation length.