# Qwen2.5 0.5B Instruct on an Apple M4 Pro (48GB)

Yes — the unquantized weights fit with room for 32k tokens of context.

Canonical page: https://makerportal.ai/lab/llm-vram/qwen2-5-0-5b-instruct/apple-m4-pro-48gb
Page title: Qwen2.5 0.5B Instruct VRAM on M4 Pro 48GB — fits at BF16

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:** Fits at FP16 — 36.0 GiB usable of 48 GB
- **Usable memory:** 36.0 GiB — 75% of the card's 48 GB — an assumption for unified memory, not a single fraction applied to every device
- **Weights at FP16:** 942 MiB — 494M parameters
- **Weights (Q4_K_M):** 379 MiB — 942 MiB at FP16 · 494M params · 6 of 6 quant rows are measured files
- **KV cache:** 12.0 KiB per token at FP16 — 24 layers × 2 KV heads × 64 dimensions
- **Speed ceiling:** 251 tok/s — 273 GB/s ÷ bytes read per token
- **Largest context that fits:** 32,768 tokens at FP16 / BF16
- **Trained context:** 32,768 — the cap the KV-cache figures are held to
- **Card bandwidth:** 273 GB/s — 48 GB LPDDR5X unified

## Every quant, against this card

Every quantization of Qwen2.5 0.5B Instruct against the 36.0 GiB usable on an Apple M4 Pro (48GB). "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 | 942 MiB | measured file | yes | 32,768 (model cap) | 251 tok/s |
| Q8_0 | 8.60 | 506 MiB | measured file | yes | 32,768 (model cap) | 432 tok/s |
| Q6_K | 8.19 | 482 MiB | measured file | yes | 32,768 (model cap) | 450 tok/s |
| Q5_K_M | 6.80 | 401 MiB | measured file | yes | 32,768 (model cap) | 524 tok/s |
| Q4_K_M | 6.44 | 379 MiB | measured file | yes | 32,768 (model cap) | 548 tok/s |
| Q3_K_M | 5.76 | 339 MiB | measured file | yes | 32,768 (model cap) | 599 tok/s |

## Questions this page answers

### How much VRAM does Qwen2.5 0.5B Instruct need?

942 MiB for the weights at FP16 — 494M parameters at two bytes each — and 379 MiB at Q4_K_M. The KV cache is on top of that and is not a fixed number: this model spends 12.0 KiB per token of context, so 8,192 tokens costs a further 96 MiB. An Apple M4 Pro (48GB) makes 36.0 GiB of its 48 GB available on the assumption below.

### Can an Apple M4 Pro (48GB) run Qwen2.5 0.5B Instruct?

Yes — the unquantized weights fit with room for 32k tokens of context. That is the weights and the KV cache together, against 36.0 GiB of usable memory.

### How fast will Qwen2.5 0.5B Instruct run on an Apple M4 Pro (48GB)?

No faster than 251 tokens/second at FP16 / BF16, and in practice below it. Decoding is memory-bound: every token reads all 942 MiB of weights plus the cache, and this card moves 273 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 Qwen2.5 0.5B Instruct 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 24 layers and 2 key/value heads of 64 dimensions, shared across 14 query heads — grouped-query attention, which divides the cache by 7. That works out at 12.0 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: 75% here, which is what this lane assumes for unified memory, where the OS and window server share the same pool — the other class assumes 92%, 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/qwen2-5-0-5b-instruct/apple-m4-pro-48gb. Free to quote and cite with attribution and a link to the canonical page.