Data center GPU · AMD

What AI models can an Instinct MI210 run?

64 GB is server territory. The constraint stops being memory and becomes bandwidth: everything fits, and what varies is how long you wait for it. At 1638 GB/s there is enough bandwidth to keep generation responsive for anything that fits.

Memory64 GB
Bandwidth1638 GB/s
Usable for a model63.2 GB
Runtime backendROCM
ArchitectureCDNA 2
Power300 W

The short answer

Assuming an 8k context window and default settings, these are the models worth downloading first.

See how fast it feels

Instinct MI210 running GPT OSS 120B at MXFP4

Wait for the first word115 ms
Then writes at188.1 tok/s
Whole answer1.0 s

YouWhy does my model use more memory when the conversation gets longer?

Model

Simulated from our estimate at a 512-token question, not a recording. Assumes nothing else is competing for the GPU.

Where it sits

With 64 GB it belongs to the 48 GB tier. The 48 GB guide explains what that tier runs and where it runs out, and the bandwidth guide explains why 1638 GB/s decides how fast it answers.

We do not track a price for this device, so it does not appear in the budget tools.

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 120B
GPT-OSS
116.83B (5.7B active) Runs great MXFP4 59.4 GB 188.1 t/s 64k
Gemma 4 31B Instruct
Gemma
31.27B Runs great Q8_0 34.67 GB 29.9 t/s 64k
Llama 3.3 70B Instruct
Llama
70.55B Runs well Q6_K 56.12 GB 18.4 t/s 16k
Qwen2.5 72B Instruct
Qwen
72.71B Runs well Q6_K 57.62 GB 17.9 t/s 16k
Qwen2.5 VL 72B Instruct
Qwen
73.41B Runs well Q6_K 58.15 GB 17.7 t/s 16k
Qwen3 32B
Qwen
32.76B Runs great Q8_0 34.09 GB 30.4 t/s 32k
Qwen3.5 27B
Qwen
27.78B Runs great Q8_0 27.32 GB 38 t/s 256k
Qwen3.5 122B A10B
Qwen
125.09B (8.17B active) Just fits IQ4_XS 62.22 GB 136.3 t/s 32k
Granite 4.1 30B
Granite
28.87B Runs great Q8_0 30.97 GB 33.4 t/s 128k
Qwen2.5 32B Instruct
Qwen
32.76B Runs great Q8_0 34.09 GB 30.4 t/s 32k
Qwen3 Coder Next
Qwen
79.67B (3.19B active) Runs great Q6_K 61.2 GB 220.7 t/s 64k
Llama 3 3 Nemotron Super 49B V1
Llama
49.87B Runs well Q6_K 57.82 GB 17.8 t/s 8k
Qwen2.5 Coder 32B Instruct
Qwen
32.76B Runs great Q8_0 34.09 GB 30.4 t/s 32k
Qwen3 Next 80B A3B Instruct
Qwen
81.32B (3.19B active) Just fits Q6_K 62.46 GB 220.7 t/s 32k
GLM 4.5 Air
GLM
110.47B (13.4B active) Runs great IQ4_XS 56.19 GB 73.8 t/s 32k
Gemma 3 27B Instruct
Gemma
27.43B Runs great Q8_0 28.68 GB 36.2 t/s 128k
Qwen3.5 35B A3B
Qwen
35.95B (2.9B active) Runs great Q8_0 35.63 GB 194.7 t/s 256k
Gemma 4 26B A4B Instruct
Gemma
25.81B (3.74B active) Runs great Q8_0 26.72 GB 131.1 t/s 256k
GLM 4.7 Flash
GLM
31.22B (3.66B active) Runs great Q8_0 31.33 GB 148.3 t/s 128k
Mixtral 8x7B Instruct V0.1
Mistral
46.7B (12.88B active) Runs great Q8_0 47.49 GB 43.2 t/s 32k
Qwen3 30B A3B
Qwen
30.53B (3.34B active) Runs great Q8_0 30.99 GB 147.4 t/s 32k
Mistral Small 24B Instruct 2501
Mistral
23.57B Runs great Q8_0 24.35 GB 42.7 t/s 32k
Phi 3.5 MoE Instruct
Phi
41.87B (6.64B active) Runs great Q8_0 42.71 GB 78.5 t/s 128k
Gemma 4 12B Instruct
Gemma
11.96B Runs great Q8_0 13.55 GB 77.7 t/s 256k
Qwen3 14B
Qwen
14.77B Runs great Q8_0 15.54 GB 67.7 t/s 32k
GPT OSS 20B
GPT-OSS
20.91B (4.18B active) Runs great MXFP4 11.55 GB 248.2 t/s 128k
Qwen3 Coder 30B A3B Instruct
Qwen
30.53B (3.34B active) Runs great Q8_0 30.99 GB 147.4 t/s 32k
Qwen2.5 14B Instruct
Qwen
14.77B Runs great Q8_0 15.79 GB 66.6 t/s 32k
Qwen2.5 Coder 14B Instruct
Qwen
14.77B Runs great Q8_0 15.79 GB 66.6 t/s 32k
Codestral 22B V0.1
Mistral
22.25B Runs great Q8_0 24.04 GB 43.4 t/s 32k
Show the other 56 models
Qwen3.5 9B 9.65B Runs great Q8_0 9.25 GB 115.6 t/s 256k
Phi 4 14.66B Runs great Q8_0 16 GB 65.7 t/s 16k
Gemma 3 12B Instruct 12.19B Runs great Q8_0 13.27 GB 79.4 t/s 128k
Gemma 4 E4B Instruct 8B Runs great Q8_0 8.42 GB 126.8 t/s 128k
Granite 4.1 8B 8.79B Runs great Q8_0 10.35 GB 102.7 t/s 128k
Qwen3 8B 8.19B Runs great Q8_0 9.02 GB 118.7 t/s 32k
Mistral Nemo Instruct 2407 12.25B Runs great Q8_0 13.15 GB 80.4 t/s 128k
Qwen2.5 VL 7B Instruct 8.29B Runs great Q8_0 8.49 GB 126.2 t/s 64k
Qwen2.5 7B Instruct 7.62B Runs great Q8_0 7.83 GB 137.4 t/s 32k
Llama 3.1 8B Instruct 8.03B Runs great Q8_0 8.83 GB 121.3 t/s 128k
DeepSeek Coder V2 Lite Instruct 15.71B Runs great Q8_0 15.91 GB 203.5 t/s 128k
Gemma 2 9B Instruct 9.24B Runs great Q8_0 11.5 GB 92 t/s 8k
Qwen2.5 Coder 7B Instruct 7.62B Runs great Q8_0 7.83 GB 137.4 t/s 32k
OLMo 3 7B Instruct 7.3B Runs great Q8_0 9.72 GB 109.7 t/s 64k
Internlm3 8B Instruct 8.8B Runs great Q8_0 8.97 GB 119.4 t/s 32k
Mistral 7B Instruct V0.3 7.25B Runs great Q8_0 8.45 GB 127.1 t/s 32k
Gemma 4 E2B Instruct 5.12B Runs great Q8_0 5.44 GB 199.7 t/s 128k
Qwen3.5 4B 4.66B Runs great Q8_0 5.27 GB 208.2 t/s 256k
Mistral 7B Instruct V0.2 7.24B Runs great Q8_0 8.44 GB 127.2 t/s 32k
DeepSeek Coder 7B Instruct V1.5 6.91B Runs great Q8_0 10.58 GB 100.5 t/s 4k
Qwen3 235B A22B 235.09B Fits only at 2 to 3 bits IQ2_XXS 58.1 GB 87.6 t/s 32k
DeepSeek Coder 6.7B Instruct 6.74B Runs great Q8_0 10.94 GB 97 t/s 16k
LFM2.5 8B A1B 8.47B Runs great Q8_0 8.81 GB 359.9 t/s 64k
Qwen3 4B 4.02B Runs great Q8_0 5.46 GB 200.3 t/s 32k
Gemma 3 4B Instruct 4.3B Runs great Q8_0 4.91 GB 224.5 t/s 128k
Phi 4 Mini Instruct 3.84B Runs great Q8_0 5.17 GB 212.9 t/s 128k
Granite 4.1 3B 3.4B Runs great Q8_0 4.34 GB 256.2 t/s 128k
Qwen2.5 3B Instruct 3.09B Runs great Q8_0 3.68 GB 306.1 t/s 32k
Phi 3 Mini 4k Instruct 3.82B Runs great Q8_0 4.8 GB 230.6 t/s 4k
Llama 3.2 3B Instruct 3.21B Runs great Q8_0 4.42 GB 252.3 t/s 128k
LFM2.5 2.6B 2.7B Runs great Q8_0 3.14 GB 365.5 t/s 128k
Qwen3.5 2B 2.27B Runs great Q8_0 2.7 GB 433.4 t/s 256k
Gemma 2 2B Instruct 2.61B Runs great Q8_0 3.54 GB 320.2 t/s 8k
Starcoder2 3B 3.03B Runs great Q8_0 3.34 GB 344.6 t/s 16k
Qwen3 1.7B 2.03B Runs great Q8_0 3.22 GB 354.4 t/s 32k
DeepSeek R1 Distill Qwen 1.5B 1.78B Runs great Q8_0 2.07 GB 584.3 t/s 128k
Qwen2.5 1.5B Instruct 1.54B Runs great Q8_0 2.06 GB 586.5 t/s 32k
SmolLM2 1.7B 1.71B Runs great Q8_0 3.53 GB 320.2 t/s 8k
OLMo 2 0425 1B 1.48B Runs great Q8_0 2.6 GB 452 t/s 4k
Llama 3.2 1B Instruct 1.24B Runs great Q8_0 1.82 GB 692 t/s 128k
LFM2.5 1.2B Instruct 1.17B Runs great Q8_0 1.46 GB 913.7 t/s 64k
Gemma 3 1B Instruct 1B Runs great Q8_0 1.35 GB 983.9 t/s 32k
Qwen3.5 0.8B 0.87B Runs great Q8_0 1.28 GB 1050.7 t/s 256k
Qwen3 0.6B 0.75B Runs great Q8_0 1.92 GB 632 t/s 32k
Qwen2.5 0.5B Instruct 0.49B Runs great Q8_0 0.88 GB 1766.4 t/s 32k
LFM2.5 350M 0.35B Runs great Q8_0 0.68 GB 2735.2 t/s 64k
MiniMax M2.5 228.7B Partial offload n/a 130.56 GB 12.5 t/s n/a
MiniMax M2.7 228.69B Partial offload n/a 130.55 GB 12.5 t/s n/a
MiniMax M3 427.04B Partial offload n/a 240.84 GB 5 t/s n/a
GLM 4.5 358.34B Partial offload n/a 204.37 GB 3.8 t/s n/a
DeepSeek V3.2 685.4B Partial offload n/a 385.91 GB 2.8 t/s n/a
DeepSeek R1 684.53B Partial offload n/a 385.42 GB 2.8 t/s n/a
Kimi K2.6 1026.88B Partial offload n/a 577.78 GB 2.7 t/s n/a
Kimi K2 Instruct 1026.41B Partial offload n/a 577.52 GB 2.7 t/s n/a
GLM 5.1 753.86B Partial offload n/a 424.51 GB 2.5 t/s n/a
GLM 5.2 753.33B Partial offload n/a 424.21 GB 2.5 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 10 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.

DeviceMemoryBandwidthModels that fit
RTX PRO 6000 Blackwell 96 GB 1792 GB/s 76 Check price
Ryzen AI Max+ 395 96GB 96 GB 256 GB/s 69 Check price
NVIDIA DGX Spark 128GB 128 GB 273 GB/s 75 Check price
Ryzen AI Max+ 395 128GB 128 GB 256 GB/s 75 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 64 GB

Same capacity, different speed. Once a model fits, bandwidth is what separates these.

Questions people ask about an Instinct MI210

How much VRAM does an Instinct MI210 have?

The Instinct MI210 has 64 GB of VRAM, of which roughly 63.2 GB is available to a model once the operating system takes its share.

Can an Instinct MI210 run a 70B model?

Yes. At Q6_K a 70B model fits in 56.12 GB and generates around 18.4 tokens per second.

What is the best local model for an Instinct MI210?

GPT OSS 120B at MXFP4 is the strongest all-round fit: 59.4 GB used, around 188.1 tokens per second at 8k context.