Desktop GPU · AMD
What AI models can a Radeon RX 7600 XT run?
16 GB covers almost everything people actually use day to day, including the 24B to 32B class that most reviewers consider the practical ceiling for a single consumer card. Bandwidth is the weak point at 288 GB/s. Models fit, then generate slowly, because every token means reading the whole active model out of memory.
The short answer
Assuming an 8k context window and default settings, these are the models worth downloading first.
Best all-rounder
GPT OSS 20B
MXFP4 · 11.55 GB · about 43.6 tokens/s
Best for code
Qwen2.5 Coder 14B Instruct
Q6_K · 12.63 GB · about 14.7 tokens/s
Largest that still runs well
GLM 4.7 Flash
31.22B parameters · Q3_K_M · about 50.5 tokens/s
Fastest useful answer
LFM2.5 8B A1B
about 63.3 tokens/s · 8.81 GB
See how fast it feels
Radeon RX 7600 XT running GPT OSS 20B at MXFP4
YouWhy does my model use more memory when the conversation gets longer?
Reading your question
Simulated from our estimate at a 512-token question, not a recording. Assumes nothing else is competing for the GPU.
Where it sits
With 16 GB it belongs to the 16 GB tier. The 16 GB guide explains what that tier runs and where it runs out, and the bandwidth guide explains why 288 GB/s decides how fast it answers.
Our indicative price for this card is $320 new, reviewed 2026-09-01. That is 5.0 GB of memory and 90 GB/s of bandwidth for every $100. Prices move, so treat these as a place to start a search and not a quote.
Devices in a similar price band, fastest memory first:
| Device | Price | Memory | Bandwidth | Best all-round pick |
|---|---|---|---|---|
| GeForce RTX 3080 12GB | $400 used | 12 GB | 912 GB/s | Gemma 4 12B Instruct, 64.2 t/s |
| GeForce RTX 3080 10GB | $350 used | 10 GB | 760 GB/s | Qwen3.5 9B, 81.1 t/s |
| GeForce RTX 2080 Ti | $250 used | 11 GB | 616 GB/s | Qwen3.5 9B, 51.3 t/s |
| GeForce RTX 3070 Ti | $300 used | 8 GB | 608 GB/s | Qwen3.5 9B, 74.4 t/s |
Every model, scored on this device
Each row uses the highest-quality quantization that both fits and stays conversational. Speed is a single-stream estimate at 8k context.
| Model | Params | Verdict | Download | Memory used | Speed | Max context |
|---|---|---|---|---|---|---|
| GPT OSS 20B GPT-OSS | 20.91B (4.18B active) | Runs great | MXFP4 | 11.55 GB | 43.6 t/s | 128k |
| Qwen3.5 9B Qwen | 9.65B | Runs well | Q8_0 | 9.25 GB | 20.3 t/s | 128k |
| Gemma 4 26B A4B Instruct Gemma | 25.81B (3.74B active) | Runs great | IQ4_XS | 13.96 GB | 39 t/s | 16k |
| Qwen3.5 27B Qwen | 27.78B | Runs well | IQ4_XS | 14.2 GB | 13.1 t/s | 16k |
| Qwen3 14B Qwen | 14.77B | Runs well | Q6_K | 12.38 GB | 15.1 t/s | 16k |
| Gemma 4 12B Instruct Gemma | 11.96B | Runs well | Q8_0 | 13.55 GB | 13.7 t/s | 16k |
| Gemma 4 E4B Instruct Gemma | 8B | Runs well | Q8_0 | 8.42 GB | 22.3 t/s | 128k |
| Qwen3 8B Qwen | 8.19B | Runs well | Q8_0 | 9.02 GB | 20.9 t/s | 32k |
| Qwen2.5 14B Instruct Qwen | 14.77B | Runs well | Q6_K | 12.63 GB | 14.7 t/s | 16k |
| Qwen2.5 Coder 14B Instruct Qwen | 14.77B | Runs well | Q6_K | 12.63 GB | 14.7 t/s | 16k |
| Qwen2.5 VL 7B Instruct Qwen | 8.29B | Runs well | Q8_0 | 8.49 GB | 22.2 t/s | 64k |
| Granite 4.1 8B Granite | 8.79B | Runs well | Q8_0 | 10.35 GB | 18.1 t/s | 32k |
| GLM 4.7 Flash GLM | 31.22B (3.66B active) | Just fits | Q3_K_M | 14.82 GB | 50.5 t/s | 8k |
| Phi 4 Phi | 14.66B | Runs well | Q6_K | 12.81 GB | 14.5 t/s | 16k |
| Gemma 3 27B Instruct Gemma | 27.43B | Just fits | IQ4_XS | 15.11 GB | 12.3 t/s | 8k |
| Qwen2.5 7B Instruct Qwen | 7.62B | Runs well | Q8_0 | 7.83 GB | 24.2 t/s | 32k |
| Llama 3.1 8B Instruct Llama | 8.03B | Runs well | Q8_0 | 8.83 GB | 21.3 t/s | 32k |
| Gemma 3 12B Instruct Gemma | 12.19B | Runs well | Q8_0 | 13.27 GB | 14 t/s | 32k |
| Qwen3 30B A3B Qwen | 30.53B (3.34B active) | Just fits | Q3_K_M | 14.84 GB | 46.2 t/s | 8k |
| DeepSeek Coder V2 Lite Instruct DeepSeek | 15.71B (2.74B active) | Runs great | Q6_K | 12.41 GB | 45.3 t/s | 64k |
| Mistral Small 24B Instruct 2501 Mistral | 23.57B | Just fits | Q4_K_M | 14.57 GB | 12.7 t/s | 8k |
| Qwen2.5 Coder 7B Instruct Qwen | 7.62B | Runs well | Q8_0 | 7.83 GB | 24.2 t/s | 32k |
| Qwen3 Coder 30B A3B Instruct Qwen | 30.53B (3.34B active) | Just fits | Q3_K_M | 14.84 GB | 46.2 t/s | 8k |
| Gemma 4 31B Instruct Gemma | 31.27B | Fits only at 2 to 3 bits | IQ3_XXS ! | 14.87 GB | 12.5 t/s | 8k |
| Mistral Nemo Instruct 2407 Mistral | 12.25B | Runs well | Q8_0 | 13.15 GB | 14.1 t/s | 16k |
| OLMo 3 7B Instruct OLMo | 7.3B | Runs well | Q8_0 | 9.72 GB | 19.3 t/s | 32k |
| Internlm3 8B Instruct InternLM | 8.8B | Runs well | Q8_0 | 8.97 GB | 21 t/s | 32k |
| Mistral 7B Instruct V0.3 Mistral | 7.25B | Runs well | Q8_0 | 8.45 GB | 22.3 t/s | 32k |
| Qwen3 32B Qwen | 32.76B | Fits only at 2 to 3 bits | IQ3_XXS ! | 13.83 GB | 13.4 t/s | 8k |
| Gemma 4 E2B Instruct Gemma | 5.12B | Runs great | Q8_0 | 5.44 GB | 35.1 t/s | 128k |
Show the other 56 models
| Gemma 2 9B Instruct | 9.24B | Runs well | Q8_0 | 11.5 GB | 16.2 t/s | 8k |
| Codestral 22B V0.1 | 22.25B | Just fits | Q4_K_M | 14.62 GB | 12.7 t/s | 8k |
| Qwen3.5 4B | 4.66B | Runs great | Q8_0 | 5.27 GB | 36.6 t/s | 256k |
| Granite 4.1 30B | 28.87B | Fits only at 2 to 3 bits | IQ3_XXS | 12.69 GB | 14.6 t/s | 16k |
| Mistral 7B Instruct V0.2 | 7.24B | Runs well | Q8_0 | 8.44 GB | 22.4 t/s | 32k |
| Qwen2.5 32B Instruct | 32.76B | Fits only at 2 to 3 bits | IQ3_XXS | 13.83 GB | 13.4 t/s | 8k |
| Qwen2.5 Coder 32B Instruct | 32.76B | Fits only at 2 to 3 bits | IQ3_XXS | 13.83 GB | 13.4 t/s | 8k |
| DeepSeek Coder 7B Instruct V1.5 | 6.91B | Runs well | Q8_0 | 10.58 GB | 17.7 t/s | 4k |
| LFM2.5 8B A1B | 8.47B | Runs great | Q8_0 | 8.81 GB | 63.3 t/s | 64k |
| Qwen3 4B | 4.02B | Runs great | Q8_0 | 5.46 GB | 35.2 t/s | 32k |
| DeepSeek Coder 6.7B Instruct | 6.74B | Runs well | Q8_0 | 10.94 GB | 17.1 t/s | 16k |
| Gemma 3 4B Instruct | 4.3B | Runs great | Q8_0 | 4.91 GB | 39.5 t/s | 128k |
| Phi 4 Mini Instruct | 3.84B | Runs great | Q8_0 | 5.17 GB | 37.4 t/s | 64k |
| Granite 4.1 3B | 3.4B | Runs great | Q8_0 | 4.34 GB | 45 t/s | 128k |
| Qwen2.5 3B Instruct | 3.09B | Runs great | Q8_0 | 3.68 GB | 53.8 t/s | 32k |
| Phi 3 Mini 4k Instruct | 3.82B | Runs great | Q8_0 | 4.8 GB | 40.5 t/s | 4k |
| Llama 3.2 3B Instruct | 3.21B | Runs great | Q8_0 | 4.42 GB | 44.4 t/s | 64k |
| LFM2.5 2.6B | 2.7B | Runs great | Q8_0 | 3.14 GB | 64.3 t/s | 128k |
| Qwen3.5 2B | 2.27B | Runs great | Q8_0 | 2.7 GB | 76.2 t/s | 256k |
| Mixtral 8x7B Instruct V0.1 | 46.7B | Fits only at 2 to 3 bits | IQ2_XXS | 12.57 GB | 25.5 t/s | 16k |
| Gemma 2 2B Instruct | 2.61B | Runs great | Q8_0 | 3.54 GB | 56.3 t/s | 8k |
| Starcoder2 3B | 3.03B | Runs great | Q8_0 | 3.34 GB | 60.6 t/s | 16k |
| Qwen3 1.7B | 2.03B | Runs great | Q8_0 | 3.22 GB | 62.3 t/s | 32k |
| DeepSeek R1 Distill Qwen 1.5B | 1.78B | Runs great | Q8_0 | 2.07 GB | 102.7 t/s | 128k |
| Qwen2.5 1.5B Instruct | 1.54B | Runs great | Q8_0 | 2.06 GB | 103.1 t/s | 32k |
| SmolLM2 1.7B | 1.71B | Runs great | Q8_0 | 3.53 GB | 56.3 t/s | 8k |
| OLMo 2 0425 1B | 1.48B | Runs great | Q8_0 | 2.6 GB | 79.5 t/s | 4k |
| Llama 3.2 1B Instruct | 1.24B | Runs great | Q8_0 | 1.82 GB | 121.7 t/s | 128k |
| LFM2.5 1.2B Instruct | 1.17B | Runs great | Q8_0 | 1.46 GB | 160.6 t/s | 64k |
| Gemma 3 1B Instruct | 1B | Runs great | Q8_0 | 1.35 GB | 173 t/s | 32k |
| Qwen3.5 0.8B | 0.87B | Runs great | Q8_0 | 1.28 GB | 184.7 t/s | 256k |
| Qwen3 0.6B | 0.75B | Runs great | Q8_0 | 1.92 GB | 111.1 t/s | 32k |
| Qwen2.5 0.5B Instruct | 0.49B | Runs great | Q8_0 | 0.88 GB | 310.6 t/s | 32k |
| LFM2.5 350M | 0.35B | Runs great | Q8_0 | 0.68 GB | 480.9 t/s | 64k |
| Qwen3.5 35B A3B | 35.95B | Partial offload | n/a | 20.48 GB | 52.9 t/s | n/a |
| Qwen3 Coder Next | 79.67B | Partial offload | n/a | 45.22 GB | 31.3 t/s | n/a |
| Qwen3 Next 80B A3B Instruct | 81.32B | Partial offload | n/a | 46.15 GB | 30.8 t/s | n/a |
| GPT OSS 120B | 116.83B | Partial offload | n/a | 59.4 GB | 16.5 t/s | n/a |
| Phi 3.5 MoE Instruct | 41.87B | Partial offload | n/a | 24.87 GB | 13.6 t/s | n/a |
| Qwen3.5 122B A10B | 125.09B | Partial offload | n/a | 70.61 GB | 11.9 t/s | n/a |
| GLM 4.5 Air | 110.47B | Partial offload | n/a | 63.61 GB | 6.5 t/s | n/a |
| MiniMax M2.5 | 228.7B | Partial offload | n/a | 130.56 GB | 6.2 t/s | n/a |
| MiniMax M2.7 | 228.69B | Partial offload | n/a | 130.55 GB | 6.2 t/s | n/a |
| Qwen3 235B A22B | 235.09B | Partial offload | n/a | 133.71 GB | 3.3 t/s | n/a |
| MiniMax M3 | 427.04B | Partial offload | n/a | 240.84 GB | 3.2 t/s | n/a |
| GLM 4.5 | 358.34B | Partial offload | n/a | 204.37 GB | 2.3 t/s | n/a |
| DeepSeek V3.2 | 685.4B | Partial offload | n/a | 385.91 GB | 2.1 t/s | n/a |
| DeepSeek R1 | 684.53B | Partial offload | n/a | 385.42 GB | 2.1 t/s | n/a |
| Kimi K2.6 | 1026.88B | Partial offload | n/a | 577.78 GB | 2 t/s | n/a |
| Kimi K2 Instruct | 1026.41B | Partial offload | n/a | 577.52 GB | 2 t/s | n/a |
| GLM 5.1 | 753.86B | Partial offload | n/a | 424.51 GB | 2 t/s | n/a |
| GLM 5.2 | 753.33B | Partial offload | n/a | 424.21 GB | 1 t/s | n/a |
| Qwen2.5 VL 72B Instruct | 73.41B | Partial offload | n/a | 43.62 GB | 1 t/s | n/a |
| Qwen2.5 72B Instruct | 72.71B | Partial offload | n/a | 43.22 GB | 1 t/s | n/a |
| Llama 3.3 70B Instruct | 70.55B | Partial offload | n/a | 42.12 GB | 1 t/s | n/a |
| Llama 3 3 Nemotron Super 49B V1 | 49.87B | Partial offload | n/a | 47.99 GB | 0.8 t/s | n/a |
What it will not run
0 of the 86 architectures we track are out of reach here, even at 2-bit precision. Another 22 run by splitting the model between the card and system RAM, which works but drops generation to single digits for most dense models.
If you want more headroom
The next steps up in memory, in order. More memory changes which models load at all; more bandwidth changes how fast they answer, so the two are worth weighing separately.
| Device | Memory | Bandwidth | Models that fit | |
|---|---|---|---|---|
| Radeon RX 7900 XT | 20 GB | 800 GB/s | 64 | Check price |
| GeForce RTX 4090 | 24 GB | 1008 GB/s | 69 | Check price |
| GeForce RTX 3090 Ti | 24 GB | 1008 GB/s | 69 | Check price |
| Radeon RX 7900 XTX | 24 GB | 960 GB/s | 69 | Check price |
Price links go to an Amazon search for the model name and are affiliate links: if you buy through one, we earn a commission at no cost to you. We do not take payment for placement, and the ordering above is by memory capacity alone.
Other devices with 16 GB
Same capacity, different speed. Once a model fits, bandwidth is what separates these.
Questions people ask about an RX 7600 XT
How much VRAM does a Radeon RX 7600 XT have?
The Radeon RX 7600 XT has 16 GB of VRAM, of which roughly 15.2 GB is available to a model once the operating system takes its share.
Can a Radeon RX 7600 XT run a 70B model?
Only partially. About 48 of 48 layers fit on the card and the rest run on the CPU, which brings generation down to roughly 31.3 tokens per second.
What is the best local model for a Radeon RX 7600 XT?
GPT OSS 20B at MXFP4 is the strongest all-round fit: 11.55 GB used, around 43.6 tokens per second at 8k context.