AMD Radeon RX 7900 GRE
The AMD Radeon RX 7900 GRE has 16 GB VRAM and 576 GB/s memory bandwidth. It can run 49 of our 110 tracked models natively in VRAM at 8k context.
With 16 GB GDDR6, the AMD Radeon RX 7900 GRE is a consumer-tier GPU that can run 49 models natively.
AMD Radeon RX 7900 GRE: 2023 RDNA 3 with 16GB GDDR6 at 576 GB/s, a China/OEM-focused trim slotted between the RX 7900 XT and the RX 7800 XT.
7B-14B models fit natively at Q4. 27B models are tight or need CPU offload. ~7-11 t/s for 7B Q4.
ROCm Linux support with llama.cpp compiled for AMD. Windows users should use the Vulkan backend. Same 16GB-class model compatibility as the RTX 4060 Ti 16GB.
| Vendor | AMD |
| Architecture | RDNA 3 |
| VRAM | 16 GB |
| Memory type | GDDR6 |
| Memory bandwidth | 576 GB/s |
| Compute backend | ROCM |
| Tier | Consumer |
| Released | 2023 |
| Models (native) | 49 / 110 |
| Models (offload) | 15 / 110 |
Need more than 16GB? Rent an RTX 3090 from $0.22/hr ↗
Prices as of Oct 17, 2026. Rates change; check RunPod for current pricing. Affiliate link: CanItRun may earn a commission. Doesn't affect the fit calculation above.
Popular models for this GPU
Models this GPU runs natively in VRAM (49)
- Qwen 3.5 35B-A3B (MoE)35B · MMLU-Pro 85.3Q2_K · ~102.2 t/s
- Qwen 3.6 35B-A3B (MoE)35B · MMLU-Pro 85.2Q2_K · ~102.2 t/s
- Ornith 1.5 35B-A3B (MoE)35B · MMLU-Pro N/AQ2_K · ~102.2 t/s
- Gemma 4 31B30.7B · MMLU-Pro 85.2Q2_K · ~33.7 t/s
- Qwen3 30B-A3B (MoE)30B · MMLU-Pro 61.5Q2_K · ~80.1 t/s
Show 44 more
- Muse Glimmer 30B27.8B · MMLU-Pro N/AQ3_K_M · ~33.5 t/s
- Gemma 2 27B Instruct27.2B · MMLU-Pro 38.0Q2_K · ~34.1 t/s
- Gemma 3 27B Instruct27B · MMLU-Pro 67.5Q2_K · ~38.3 t/s
- Qwen 3.6 27B27B · MMLU-Pro 86.2Q3_K_M · ~32.8 t/s
- UI-Mate 27B27B · MMLU-Pro ~86.2Q3_K_M · ~32.8 t/s
- Bonsai 27B27B · MMLU-Pro ~81.5Ternary (Q2_0) · ~52.3 t/s
- Bonsai 2 27B27B · MMLU-Pro N/ATernary (Q2_0) · ~60.5 t/s
- Qwen 3.8 27B27B · MMLU-Pro N/AQ3_K_M · ~32.8 t/s
- Gemma 4 26B (MoE)25.2B · MMLU-Pro 82.6Q3_K_M · ~102.6 t/s
- Mistral Small 3.1 24B Instruct24B · MMLU-Pro 66.8Q3_K_M · ~35.9 t/s
- Mistral Small 22B22.2B · MMLU-Pro 49.2Q3_K_M · ~35.5 t/s
- GPT-OSS 20B21B · MMLU-Pro 67.9Q4_K_M · ~110.5 t/s
- Qwen3 14B14.8B · MMLU-Pro 61.0Q6_K · ~34.5 t/s
- Qwen 2.5 14B Instruct14.7B · MMLU-Pro 63.7Q5_K_M · ~37.4 t/s
- Phi-4 14B Instruct14B · MMLU-Pro 70.4Q6_K · ~35.2 t/s
- Mistral Nemo 12B Instruct12.2B · MMLU-Pro 35.6Q6_K · ~40.3 t/s
- Gemma 3 12B Instruct12.2B · MMLU-Pro 60.6Q6_K · ~41 t/s
- Gemma 4 12B (Unified)12B · MMLU-Pro 77.2Q8_0 · ~34.5 t/s
- Gemma 2 9B Instruct9.2B · MMLU-Pro 32.0Q8_0 · ~36.5 t/s
- Qwen 3.5 9B9B · MMLU-Pro 82.5Q8_0 · ~46.9 t/s
- Bonsai 2 9B9B · MMLU-Pro N/ATernary (Q2_0) · ~133.3 t/s
- Ornith 1.5 9B9B · MMLU-Pro N/AQ8_0 · ~46.9 t/s
- Llama 3.1 8B Instruct8B · MMLU-Pro 48.3Q8_0 · ~48 t/s
- DeepSeek R1 Distill Llama 8B8B · MMLU-Pro 41.0Q8_0 · ~48 t/s
- Qwen3 8B8B · MMLU-Pro 56.7Q8_0 · ~46.9 t/s
- Qwen 2.5 7B Instruct7.6B · MMLU-Pro 56.3Q8_0 · ~53.9 t/s
- Gemma 4 E4B7.52B · MMLU-Pro 69.4Q8_0 · ~81.5 t/s
- Mistral 7B Instruct v0.37.25B · MMLU-Pro 30.0Q8_0 · ~52 t/s
- Gemma 4 E2B4.65B · MMLU-Pro 60.0BF16 · ~88.6 t/s
- Gemma 3 4B Instruct4B · MMLU-Pro 43.6BF16 · ~53.8 t/s
- Bonsai 2 4B4B · MMLU-Pro N/ATernary (Q2_0) · ~206.8 t/s
- Phi-3.5 Mini Instruct3.8B · MMLU-Pro 47.4BF16 · ~43 t/s
- Phi-4-mini Instruct3.8B · MMLU-Pro 67.3BF16 · ~52.5 t/s
- Llama 3.2 3B Instruct3.2B · MMLU-Pro 24.0BF16 · ~61.9 t/s
- Qwen 2.5 3B Instruct3.1B · MMLU-Pro 32.4BF16 · ~66.7 t/s
- Gemma 2 2B Instruct2.6B · MMLU-Pro 17.8BF16 · ~73.9 t/s
- Bonsai 2 2B2B · MMLU-Pro N/ATernary (Q2_0) · ~367.2 t/s
- LFM3 1.7B1.73B · MMLU-Pro N/ABF16 · ~118.1 t/s
- SmolLM2 1.7B Instruct1.7B · MMLU-Pro 19.0BF16 · ~88.3 t/s
- Qwen 2.5 1.5B Instruct1.5B · MMLU-Pro 16.8BF16 · ~124 t/s
- Llama 3.2 1B Instruct1.24B · MMLU-Pro 12.5BF16 · ~156.7 t/s
- Gemma 3 1B Instruct1B · MMLU-Pro 14.7BF16 · ~162.6 t/s
- Qwen 2.5 0.5B Instruct0.5B · MMLU-Pro 10.0BF16 · ~276.9 t/s
- SmolLM2 360M Instruct0.36B · MMLU-Pro 8.0BF16 · ~249.8 t/s
Models that fit with CPU offload (15)
These use system RAM for layers that don't fit in VRAM, so expect much slower inference.
- Qwen 2.5 72B Instruct72B · MMLU-Pro 71.1Q2_K · ~1.6 t/s
- Llama 3.3 70B Instruct70B · MMLU-Pro 68.9Q3_K_M · ~1.1 t/s
- DeepSeek R1 Distill Llama 70B70B · MMLU-Pro 70.0Q3_K_M · ~1.1 t/s
- Llama 3.1 70B Instruct70B · MMLU-Pro 66.4Q3_K_M · ~1.1 t/s
- Mixtral 8x7B Instruct v0.146.7B · MMLU-Pro 29.7Q5_K_M · ~5.1 t/s
- Flint 40B (MoE)39.8B · MMLU-Pro N/AQ6_K · ~39.9 t/s
- Command-R 35B35B · MMLU-Pro 33.0Q5_K_M · ~1.2 t/s
- Yi 1.5 34B Chat34.4B · MMLU-Pro 37.0Q6_K · ~1.6 t/s
- Nanbeige4.3 33B33.1B · MMLU-Pro N/AQ6_K · ~1.5 t/s
- Qwen3 32B32.8B · MMLU-Pro 65.5Q8_0 · ~1.1 t/s
- Qwen 2.5 32B Instruct32.5B · MMLU-Pro 69.0Q8_0 · ~1.1 t/s
- Qwen 2.5 Coder 32B Instruct32.5B · MMLU-Pro 62.3Q8_0 · ~1.1 t/s
- DeepSeek R1 Distill Qwen 32B32.5B · MMLU-Pro 65.0Q8_0 · ~1.1 t/s
- Nemotron 3 Nano 30B32B · MMLU-Pro 78.3Q8_0 · ~18.5 t/s
- Nemotron 3.5 Lightning 30B-A3B30B · MMLU-Pro 81.6Q8_0 · ~16.8 t/s
Too large for this GPU (46)
- Mixtral 8x22B Instruct v0.1
- Llama 3.1 405B Instruct
- DeepSeek V3 671B
- DeepSeek R1 671B
- Llama 4 Scout 109B
- Llama 4 Maverick 400B
- Qwen3 235B-A22B (MoE)
- MiniMax M1 456B
- GPT-OSS 120B
- GLM-4.5 355B
- GLM-4.5 Air 106B
- GLM-4.6 355B
- GLM-4.6V 106B
- GLM-4.7 358B
- Qwen 3.5 122B-A10B (MoE)
- MiniMax M2.5 229B
- GLM-5 744B
- MiniMax M2.7 229B
- Nemotron 3 Super 120B
- Nemotron 3.5 Super VL 120B-A12B
- Kimi K2.6
- GLM-5.1 754B
- DeepSeek V4 Pro 1.6T
- DeepSeek V4 Flash 284B
- Mistral Medium 3.5 128B
- GLM-5.2 753B
- Nemotron 3 Ultra 550B-A55B
- Step 3.5 Flash
- Step 3.7 Flash
- MiMo V2.5 Pro
- Kimi K2.5
- MiniMax M3
- Inkling
- Kimi K3
- Kimi K3.1 Pro
- DeepSeek V4 Flash 0731 284B
- Qwen3.8 2.4T-A95B
- Qwen3.8-Flash-Next
- DeepSeek V4 Pro 0813 1.6T
- Muse Spark Open 816B-A42B
- Ornith 1.5 397B (MoE)
- GLM-5.3 753B
- GLM-5.3-Flash 320B
- DeepSeek V4.1 Flash 552B
- DeepSeek V4.1 Flash 1017 552B
- Ornith 1.5 122B (MoE)
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Frequently asked questions
- How much VRAM does the AMD Radeon RX 7900 GRE have?
- The AMD Radeon RX 7900 GRE has 16 GB of GDDR6 with 576 GB/s memory bandwidth.
- What is the AMD Radeon RX 7900 GRE best for?
- With 16 GB of VRAM, the AMD Radeon RX 7900 GRE handles smaller models (7B–14B) at Q4–Q5 quantization, ideal for entry-level local LLM experimentation and lightweight inference.
- What LLMs can the AMD Radeon RX 7900 GRE run locally?
- The AMD Radeon RX 7900 GRE can run 49 of the 110 open-weight models tracked by CanItRun natively in VRAM at 8k context. Top options include: Qwen 3.8 27B at Q3_K_M, Ornith 1.5 35B-A3B (MoE) at Q2_K, Ornith 1.5 9B at Q8_0.
- Can the AMD Radeon RX 7900 GRE run Gemma 4 31B?
- Yes. The AMD Radeon RX 7900 GRE runs Gemma 4 31B natively in VRAM at Q2_K quantization, achieving approximately 33.7 tokens per second.
- Can the AMD Radeon RX 7900 GRE run Qwen 3.6 27B?
- Yes. The AMD Radeon RX 7900 GRE runs Qwen 3.6 27B natively in VRAM at Q3_K_M quantization, achieving approximately 32.8 tokens per second.
- Can the AMD Radeon RX 7900 GRE run Qwen3 8B?
- Yes. The AMD Radeon RX 7900 GRE runs Qwen3 8B natively in VRAM at Q8_0 quantization, achieving approximately 46.9 tokens per second.