# Gemma 3 4B Instruct on a Jetson Orin Nano Super (8GB)

Yes, quantized. Q8_0 is the largest that fits, leaving room for 84k tokens of context.

Canonical page: https://makerportal.ai/lab/llm-vram/gemma-3-4b-it/jetson-orin-nano-super-8gb
Page title: Gemma 3 4B Instruct on Jetson Orin Nano 8GB — fits at Q8_0

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:** Q8_0 — 6.0 GiB usable of 8 GB
- **Usable memory:** 6.0 GiB — 75% of the card's 8 GB — an assumption for unified memory, not a single fraction applied to every device
- **Weights at FP16:** 8.0 GiB — 4.3B parameters
- **Weights (Q4_K_M):** 2.4 GiB — 8.0 GiB at FP16 · 4.3B params · 1 of 6 quant rows are measured files
- **KV cache:** 136.0 KiB per token at FP16 — 34 layers × 4 KV heads × 256 dimensions
- **Speed ceiling:** 21 tok/s — 102 GB/s ÷ bytes read per token
- **Largest context that fits:** 85,545 tokens at Q8_0
- **Trained context:** 131,072 — the cap the KV-cache figures are held to
- **Card bandwidth:** 102 GB/s — 8 GB LPDDR5 unified

## Every quant, against this card

Every quantization of Gemma 3 4B Instruct against the 6.0 GiB usable on a Jetson Orin Nano Super (8GB). "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 | 8.0 GiB | measured file | short by 2.0 GiB | — | 11.1 tok/s offloaded |
| Q8_0 | 8.50 | 4.3 GiB | nominal | yes | 85,545 | 21 tok/s |
| Q6_K | 6.56 | 3.3 GiB | nominal | yes | 131,072 (model cap) | 27 tok/s |
| Q5_K_M | 5.67 | 2.8 GiB | nominal | yes | 131,072 (model cap) | 31 tok/s |
| Q4_K_M | 4.83 | 2.4 GiB | nominal | yes | 131,072 (model cap) | 35 tok/s |
| Q3_K_M | 3.91 | 2.0 GiB | nominal | yes | 131,072 (model cap) | 43 tok/s |

## Questions this page answers

### How much VRAM does Gemma 3 4B Instruct need?

8.0 GiB for the weights at FP16 — 4.3B parameters at two bytes each — and 2.4 GiB at Q4_K_M. The KV cache is on top of that and is not a fixed number: this model spends 136.0 KiB per token of context on its full-attention layers, so 8,192 tokens costs a further 276 MiB. A Jetson Orin Nano Super (8GB) makes 6.0 GiB of its 8 GB available on the assumption below.

### Can a Jetson Orin Nano Super (8GB) run Gemma 3 4B Instruct?

Yes, quantized. Q8_0 is the largest that fits, leaving room for 84k tokens of context. That is the weights and the KV cache together, against 6.0 GiB of usable memory.

### How fast will Gemma 3 4B Instruct run on a Jetson Orin Nano Super (8GB)?

No faster than 21 tokens/second at Q8_0, and in practice below it. Decoding is memory-bound: every token reads all 4.3 GiB of weights plus the cache, and this card moves 102 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 Gemma 3 4B 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 34 layers and 4 key/value heads of 256 dimensions, shared across 8 query heads — grouped-query attention, which divides the cache by 2. That works out at 136.0 KiB per token, except that 29 of the 34 layers use a 1024-token sliding window and stop growing there. 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/gemma-3-4b-it/jetson-orin-nano-super-8gb. Free to quote and cite with attribution and a link to the canonical page.