CPU only (system RAM)
The CPU only (system RAM) has 0 GB VRAM and 80 GB/s memory bandwidth. It can run 0 of our 97 tracked models natively in VRAM at 8k context.
With 0 GB DDR4 / DDR5, the CPU only (system RAM) is a integrated-tier GPU that can run 0 models natively. 7B at Q4 ~1-5 t/s depending on AVX2/AVX-512 support. 14B ~0.5-2 t/s.
CPU only (system RAM): x86-64/ARM with DDR4/DDR5 at ~80 GB/s, CPU inference fallback.
7B at Q4 ~1-5 t/s depending on AVX2/AVX-512 support. 14B ~0.5-2 t/s.
llama.cpp CPU backend. AVX2 or AVX-512 recommended. Expect 1-5 t/s for 7B on modern desktop CPU.
| Vendor | Generic |
| Architecture | x86-64 / ARM |
| VRAM | 0 GB |
| Memory type | DDR4 / DDR5 |
| Memory bandwidth | 80 GB/s |
| Compute backend | CPU |
| Tier | Integrated |
| Released | 2024 |
| Models (native) | 0 / 97 |
| Models (offload) | 53 / 97 |
Software: llama.cpp CPU backend. AVX2 or AVX-512 recommended. Expect 1–5 t/s for 7B models on a modern desktop CPU.
Models this GPU runs natively in VRAM (0)
None.
Models that fit with CPU offload (53)
These use system RAM for layers that don't fit in VRAM, so expect much slower inference.
- Mixtral 8x7B Instruct v0.146.7B · MMLU-Pro 29.7Q3_K_M · ~1.8 t/s
- Command-R 35B35B · MMLU-Pro 33.0Q2_K · ~1.7 t/s
- Qwen 3.5 35B-A3B (MoE)35B · MMLU-Pro 85.3Q4_K_M · ~6.4 t/s
- Qwen 3.6 35B35B · MMLU-Pro 85.2Q4_K_M · ~1.7 t/s
- Ornith 1.5 35B-A3B (MoE)35B · MMLU-Pro N/AQ4_K_M · ~6.4 t/s
- Yi 1.5 34B Chat34.4B · MMLU-Pro 37.0Q4_K_M · ~1.7 t/s
- Qwen3 32B32.8B · MMLU-Pro 65.5Q5_K_M · ~1.6 t/s
- Qwen 2.5 32B Instruct32.5B · MMLU-Pro 69.0Q4_K_M · ~1.8 t/s
- Qwen 2.5 Coder 32B Instruct32.5B · MMLU-Pro 62.3Q4_K_M · ~1.8 t/s
- DeepSeek R1 Distill Qwen 32B32.5B · MMLU-Pro 65.0Q4_K_M · ~1.8 t/s
- Nemotron 3 Nano 30B32B · MMLU-Pro 78.3Q5_K_M · ~5.3 t/s
- Gemma 4 31B30.7B · MMLU-Pro 85.2Q5_K_M · ~1.7 t/s
- Qwen3 30B-A3B (MoE)30B · MMLU-Pro 61.5Q5_K_M · ~5 t/s
- Nemotron 3.5 Lightning 30B-A3B30B · MMLU-Pro 81.6Q6_K · ~4.8 t/s
- Muse Glimmer 30B27.8B · MMLU-Pro N/AQ6_K · ~1.7 t/s
- Gemma 2 27B Instruct27.2B · MMLU-Pro 38.0Q5_K_M · ~1.8 t/s
- Gemma 3 27B Instruct27B · MMLU-Pro 67.5Q6_K · ~1.7 t/s
- Qwen 3.6 27B27B · MMLU-Pro 86.2Q6_K · ~1.8 t/s
- UI-Mate 27B27B · MMLU-Pro ~86.2Q6_K · ~1.8 t/s
- Bonsai 27B27B · MMLU-Pro 81.5Ternary (Q2_0) · ~4.5 t/s
- Qwen 3.8 27B27B · MMLU-Pro N/AQ6_K · ~1.8 t/s
- Gemma 4 26B (MoE)25.2B · MMLU-Pro 82.6Q6_K · ~3.7 t/s
- Mistral Small 3.1 24B Instruct24B · MMLU-Pro 66.8Q6_K · ~1.9 t/s
- Mistral Small 22B22.2B · MMLU-Pro 49.2Q6_K · ~2 t/s
- GPT-OSS 20B21B · MMLU-Pro 67.9Q8_0 · ~3.1 t/s
- Qwen3 14B14.8B · MMLU-Pro 61.0Q8_0 · ~2.3 t/s
- Qwen 2.5 14B Instruct14.7B · MMLU-Pro 63.7Q8_0 · ~2.3 t/s
- Phi-4 14B Instruct14B · MMLU-Pro 70.4Q8_0 · ~2.5 t/s
- Mistral Nemo 12B Instruct12.2B · MMLU-Pro 35.6Q8_0 · ~2.8 t/s
- Gemma 3 12B Instruct12.2B · MMLU-Pro 60.6Q8_0 · ~2.9 t/s
- Gemma 4 12B (Unified)12B · MMLU-Pro 77.2Q8_0 · ~2.5 t/s
- Gemma 2 9B Instruct9.2B · MMLU-Pro 32.0BF16 · ~1.9 t/s
- Qwen 3.5 9B9B · MMLU-Pro 82.5BF16 · ~2.2 t/s
- Ornith 1.5 9B9B · MMLU-Pro N/ABF16 · ~2.2 t/s
- Llama 3.1 8B Instruct8B · MMLU-Pro 48.3BF16 · ~2.3 t/s
- DeepSeek R1 Distill Llama 8B8B · MMLU-Pro 41.0BF16 · ~2.3 t/s
- Qwen3 8B8B · MMLU-Pro 56.7BF16 · ~2.3 t/s
- Qwen 2.5 7B Instruct7.6B · MMLU-Pro 56.3BF16 · ~2.6 t/s
- Mistral 7B Instruct v0.37.25B · MMLU-Pro 30.0BF16 · ~2.6 t/s
- Gemma 3 4B Instruct4B · MMLU-Pro 43.6BF16 · ~4.7 t/s
- Gemma 4 E4B4B · MMLU-Pro 69.4BF16 · ~4.4 t/s
- Phi-3.5 Mini Instruct3.8B · MMLU-Pro 47.4BF16 · ~3.7 t/s
- Phi-4-mini Instruct3.8B · MMLU-Pro 67.3BF16 · ~4.6 t/s
- Llama 3.2 3B Instruct3.2B · MMLU-Pro 24.0BF16 · ~5.4 t/s
- Qwen 2.5 3B Instruct3.1B · MMLU-Pro 32.4BF16 · ~6.2 t/s
- Gemma 2 2B Instruct2.6B · MMLU-Pro 17.8BF16 · ~6.6 t/s
- Gemma 4 E2B2B · MMLU-Pro 60.0BF16 · ~9.1 t/s
- SmolLM2 1.7B Instruct1.7B · MMLU-Pro 19.0BF16 · ~8 t/s
- Qwen 2.5 1.5B Instruct1.5B · MMLU-Pro 16.8BF16 · ~12.4 t/s
- Llama 3.2 1B Instruct1.24B · MMLU-Pro 12.5BF16 · ~14.6 t/s
- Gemma 3 1B Instruct1B · MMLU-Pro 14.7BF16 · ~17.2 t/s
- Qwen 2.5 0.5B Instruct0.5B · MMLU-Pro 10.0BF16 · ~36.3 t/s
- SmolLM2 360M Instruct0.36B · MMLU-Pro 8.0BF16 · ~37.9 t/s
Too large for this GPU (44)
- Llama 3.3 70B Instruct
- Qwen 2.5 72B Instruct
- DeepSeek R1 Distill Llama 70B
- Llama 3.1 70B Instruct
- 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
- 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
- DeepSeek V4 Flash 0731 284B
- Qwen3.8 2.4T-A95B
- Qwen3.8-Flash-Next
- DeepSeek V4 Pro 0813 1.6T
- Ornith 1.5 397B (MoE)
- GLM-5.3 753B
- GLM-5.3-Flash 320B
Frequently asked questions
- How much VRAM does the CPU only (system RAM) have?
- The CPU only (system RAM) has 0 GB of DDR4 / DDR5 with 80 GB/s memory bandwidth.
- What is the CPU only (system RAM) best for?
- With 0 GB of VRAM, the CPU only (system RAM) is best for running compact models (1B–8B) at low quantization, suitable for edge inference, prototyping, and lightweight tasks.
- What LLMs can the CPU only (system RAM) run locally?
- The CPU only (system RAM) cannot run any of the 97 tracked models fully in VRAM at 8k context. It may handle smaller models with CPU offload.
- Can the CPU only (system RAM) run Gemma 4 31B?
- The CPU only (system RAM) can run Gemma 4 31B with CPU offload at Q5_K_M quantization, but inference will be slower than native VRAM execution.
- Can the CPU only (system RAM) run Qwen 3.6 27B?
- The CPU only (system RAM) can run Qwen 3.6 27B with CPU offload at Q6_K quantization, but inference will be slower than native VRAM execution.
- Can the CPU only (system RAM) run Qwen3 8B?
- The CPU only (system RAM) can run Qwen3 8B with CPU offload at BF16 quantization, but inference will be slower than native VRAM execution.