# Qwen3 30B-A3B on a GeForce RTX 4090

Yes, quantized. Q5_K_M is the largest that fits, leaving room for 20k tokens of context.

Canonical page: https://makerportal.ai/lab/llm-vram/qwen3-30b-a3b/rtx-4090
Page title: Qwen3 30B-A3B VRAM on RTX 4090 — fits at Q5_K_M

This markdown document and the HTML page above are rendered from the same solved values at build time, by the same functions. Nothing here is written by a language model and nothing is fetched at request time.

## Key figures

- **Verdict:** Q5_K_M — 22.1 GiB usable of 24 GB
- **Usable memory:** 22.1 GiB — 92% of the card's 24 GB — an assumption for a dedicated card, not a single fraction applied to every device
- **Weights at FP16:** 56.9 GiB — 30.5B parameters
- **Weights (Q4_K_M):** 17.3 GiB — 56.9 GiB at FP16 · 30.5B params · 6 of 6 quant rows are measured files
- **KV cache:** 96.0 KiB per token at FP16 — 48 layers × 4 KV heads × 128 dimensions
- **Speed ceiling:** 316 tok/s — 1008 GB/s ÷ bytes read per token
- **Largest context that fits:** 20,168 tokens at Q5_K_M
- **Trained context:** 40,960 — the cap the KV-cache figures are held to
- **Card bandwidth:** 1008 GB/s — 24 GB GDDR6X

## Every quant, against this card

Every quantization of Qwen3 30B-A3B against the 22.1 GiB usable on a GeForce RTX 4090. "Measured" rows are the byte size of a real published file; "nominal" rows are the parameter count times the published bits-per-weight.

| Precision | Bits/weight | Weights | Source | Fits | Max context | Ceiling |
|---|---|---|---|---|---|---|
| FP16 / BF16 | 16.00 | 56.9 GiB | measured file | short by 34.8 GiB | — | 17.5 tok/s offloaded |
| Q8_0 | 8.51 | 30.3 GiB | measured file | short by 8.2 GiB | — | 45.0 tok/s offloaded |
| Q6_K | 6.57 | 23.4 GiB | measured file | short by 1.3 GiB | — | 75.6 tok/s offloaded |
| Q5_K_M | 5.69 | 20.2 GiB | measured file | yes | 20,168 | 316 tok/s |
| Q4_K_M | 4.86 | 17.3 GiB | measured file | yes | 40,960 (model cap) | 355 tok/s |
| Q3_K_M | 3.85 | 13.7 GiB | measured file | yes | 40,960 (model cap) | 416 tok/s |

## Questions this page answers

### How much VRAM does Qwen3 30B-A3B need?

56.9 GiB for the weights at FP16 — 30.5B parameters at two bytes each — and 17.3 GiB at Q4_K_M. The KV cache is on top of that and is not a fixed number: this model spends 96.0 KiB per token of context, so 8,192 tokens costs a further 768 MiB. A GeForce RTX 4090 makes 22.1 GiB of its 24 GB available on the assumption below.

### Can a GeForce RTX 4090 run Qwen3 30B-A3B?

Yes, quantized. Q5_K_M is the largest that fits, leaving room for 20k tokens of context. That is the weights and the KV cache together, against 22.1 GiB of usable memory.

### How fast will Qwen3 30B-A3B run on a GeForce RTX 4090?

No faster than 316 tokens/second at Q5_K_M, and in practice below it. Decoding is memory-bound: every token reads the 11% of weights this MoE routes to 2.2 GiB of weights plus the cache, and this card moves 1008 GB/s. That division is the ceiling — no kernel, runtime or driver beats it, and a real runtime typically reaches 60–80% of it.

### Why does the context length change how much memory Qwen3 30B-A3B needs?

Because the KV cache holds one key and one value vector per token, per layer, for the whole conversation, and it is allocated separately from the weights. This model has 48 layers and 4 key/value heads of 128 dimensions, shared across 32 query heads — grouped-query attention, which divides the cache by 8. That works out at 96.0 KiB per token. Parameter count tells you nothing about this number.

## Method and limits

Weight bytes are the byte size of the real published file wherever one exists, and the model's exact parameter count times the published llama.cpp bits-per-weight where it does not. That distinction is on every row above and it matters at both ends: a sub-1B model's Q4_K_M file runs a third larger than the nominal figure because k-quants keep its embedding tables at higher precision, and an already-4-bit release cannot be quantized upward at all. The KV cache is 2 · layers · kv_heads · head_dim · bytes per token, summed over layers with each sliding-window layer capped at its window. The speed figure is a roofline bound, not a benchmark: bandwidth divided by bytes read per token, which no runtime exceeds and every runtime falls short of. The usable fraction of card memory is an assumption: 92% here, which is what this lane assumes for a dedicated card — the other class assumes 75%, so it is not one number applied to every device. That is the only assumed input on this page; every other figure is computed from the model config and the card's published specification. Nothing on this page is written by a language model.

## Related tool

[LLM VRAM & KV-Cache Footprint Calculator](https://makerportal.ai/lab/llm-vram-kvcache-calculator) — Interactive VRAM planner — change the model geometry, quantization, context length and card capacity and read the weight bytes, KV-cache bytes and the bandwidth ceiling on decode speed.

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Source: MakerPortal — https://makerportal.ai/lab/llm-vram/qwen3-30b-a3b/rtx-4090. Free to quote and cite with attribution and a link to the canonical page.