Can the NVIDIA GeForce RTX 2070 SUPER run Qwen3-4B-Instruct-2507?
IT FITS
Yes. 8 of 9 formats evaluated fit in 8 GB, at 8,192 tokens. No published file exists for this pair, so every size is rebuilt from the pinned architecture. The best quality that fits is Q8_0, needing 6.70 GB and running at an estimated 56 tokens per second.
Fitting in memory is not the same as loading. Whether the runtime and version you have supports this architecture and format on this machine has not been tested here.
Find the NVIDIA GeForce RTX 2070 SUPER: Amazon ↗ · eBay (new and used) ↗Store links may pay us a commission. They never decide which card is suggested — the memory arithmetic does.
How long a conversation. At Q8_0, this pair holds 16,994 tokens of context — roughly 12,746 words — needing 8.00 GB. Past that the NVIDIA GeForce RTX 2070 SUPER runs out of memory, not the model out of context, which would allow 262,144. This is the wall a long chat hits after it has already loaded fine. The nearest round setting below it is 16,384.
Every format evaluated
No published file exists for this pair, so every size is rebuilt from the pinned architecture. The Basis column says which is which for each row. Decode and first-token figures are calibrated estimates, not runs on this card.
| Format | Needs | Of 8 GB | Fits | Decode | First token | Basis |
|---|---|---|---|---|---|---|
| FP16 | 10.8 GB | 135% | short by 2.84 GB | does not run | — | reconstructed size |
| Q8_0 | 6.70 GB | 84% | yes | ~56 tok/s | no comparable peak | reconstructed size |
| Q6_K | 5.63 GB | 70% | yes | ~68 tok/s | no comparable peak | reconstructed size |
| Q5_K_M | 5.17 GB | 65% | yes | ~74 tok/s | no comparable peak | reconstructed size |
| Q5_0 | 5.10 GB | 64% | yes | ~76 tok/s | no comparable peak | reconstructed size |
| Q4_K_M | 4.72 GB | 59% | yes | ~82 tok/s | no comparable peak | reconstructed size |
| Q4_0 | 4.60 GB | 57% | yes | ~85 tok/s | no comparable peak | reconstructed size |
| Q3_K_M | 4.26 GB | 53% | yes | ~93 tok/s | no comparable peak | reconstructed size |
| Q2_K | 3.75 GB | 47% | yes | ~108 tok/s | no comparable peak | reconstructed size |
Sized at 8,192 tokens of context with the whole model resident — weights, the KV cache and the runtime reserve, offload off. Speed is only quoted for a format that fits: a rate for a configuration that cannot load is not a fact about anything.
Why the first-token figure is the same on every row
First token is modelled from the arithmetic the prompt requires, and that count does not change with the weight format — which is why it reads the same on every row. Real prefill does vary by format, because a quantized matmul is a different kernel; this model does not capture that, and the figure should be read as an order of magnitude rather than a ranking between formats.
What the labels mean
- published data
- Read from a published source — a file's byte count, a model's configuration or a manufacturer's specification — or exact arithmetic on such values. Not a measurement on a machine.
- reconstructed size
- Weight size reconstructed from the pinned architecture, because no published file exists.
- size range
- Only a lower and an upper bound are claimed for this weight size.
- estimate
- Calculated from sourced inputs by a stated method; an estimate, not a measurement.
This page fixes the context at 8,192 tokens and one device. Batch, concurrent users, KV-cache format, clusters and rental cost are all in the calculator, already set to this pairing.
Open in the calculator →Everything the NVIDIA GeForce RTX 2070 SUPER runsBest models for 8 GB
Qwen3-4B-Instruct-2507’s architecture is read from its publisher’s own config.json at a pinned revision, and the NVIDIA GeForce RTX 2070 SUPER’s 8 GB comes from the specification; its 448 GB/s is a third-party figure, because the manufacturer no longer publishes it, so the speed here is an estimate built on an estimate. How far each figure can be trusted is published, per format and worst case included, on the accuracy scorecard.
Building this into your own product? A free API key returns exactly these figures, and the widget puts this answer on a product page with one script tag.
Use this answer in your own product
curl -s https://llmbottleneck.com/v1/analyze \
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<script src="https://llmbottleneck.com/widget.js" data-model="qwen-qwen3-4b-instruct-2507" data-quantization="Q8_0" data-hardware="nvidia-geforce-rtx-2070-super" data-context="8192"></script>
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