Intel Data Center GPU Max 1100
The Intel Data Center GPU Max 1100 has 48 GB VRAM and 1229 GB/s memory bandwidth. It can run 57 of our 94 tracked models natively in VRAM at 8k context.
With 48 GB HBM2e, the Intel Data Center GPU Max 1100 is a datacenter-tier GPU that can run 57 models natively. It handles 70B-class models at Q4 quantization.
The Intel Data Center GPU Max 1100 offers 48GB of HBM2e at 1,229 GB/s in a lower-power (300W) package compared to the 1550. It fits 30B–34B models at Q8, or 70B models at Q3_K_M (Q4_K_M's real-world total is just past this card's effective capacity), and is used in supercomputing installations alongside CPU tiles in the Aurora system at Argonne National Laboratory.
Intel Data Center GPU Max 1100: 2022 Xe-HPC Ponte Vecchio with 48GB HBM2e at 1,229 GB/s, mid-tier Intel HPC GPU.
30B-34B at Q8, or 70B at Q3_K_M (not Q4_K_M, whose ~51GB total exceeds this card's effective capacity), fit in 48GB. Strong bandwidth for datacenter inference.
SYCL/oneAPI gives best performance; llama.cpp SYCL backend supported. Typically cloud/HPC-accessed.
| Vendor | Intel |
| Architecture | Xe-HPC (Ponte Vecchio) |
| VRAM | 48 GB |
| Memory type | HBM2e |
| Memory bandwidth | 1229 GB/s |
| Compute backend | VULKAN |
| Tier | Datacenter |
| Released | 2022 |
| Models (native) | 57 / 94 |
| Models (offload) | 8 / 94 |
Popular models for this GPU
Models this GPU runs natively in VRAM (57)
- Qwen 2.5 72B Instruct72B · MMLU-Pro 71.1Q3_K_M · ~21.4 t/s
- Llama 3.3 70B Instruct70B · MMLU-Pro 68.9Q3_K_M · ~22 t/s
- DeepSeek R1 Distill Llama 70B70B · MMLU-Pro 70.0Q3_K_M · ~22 t/s
- Llama 3.1 70B Instruct70B · MMLU-Pro 66.4Q3_K_M · ~22 t/s
- Mixtral 8x7B Instruct v0.146.7B · MMLU-Pro 29.7Q6_K · ~22 t/s
Show 52 more
- Command-R 35B35B · MMLU-Pro 33.0Q6_K · ~20.2 t/s
- Qwen 3.5 35B-A3B (MoE)35B · MMLU-Pro 85.3Q8_0 · ~74 t/s
- Qwen 3.6 35B35B · MMLU-Pro 85.2Q8_0 · ~20.3 t/s
- Ornith 1.5 35B-A3B (MoE)35B · MMLU-Pro N/AQ8_0 · ~74 t/s
- Yi 1.5 34B Chat34.4B · MMLU-Pro 37.0Q8_0 · ~20.7 t/s
- Qwen3 32B32.8B · MMLU-Pro 65.5Q8_0 · ~22.1 t/s
- Qwen 2.5 32B Instruct32.5B · MMLU-Pro 69.0Q8_0 · ~21.8 t/s
- Qwen 2.5 Coder 32B Instruct32.5B · MMLU-Pro 62.3Q8_0 · ~21.8 t/s
- DeepSeek R1 Distill Qwen 32B32.5B · MMLU-Pro 65.0Q8_0 · ~21.8 t/s
- Nemotron 3 Nano 30B32B · MMLU-Pro 78.3Q8_0 · ~72.2 t/s
- Gemma 4 31B30.7B · MMLU-Pro 85.2Q8_0 · ~23.4 t/s
- Qwen3 30B-A3B (MoE)30B · MMLU-Pro 61.5Q8_0 · ~69.9 t/s
- Nemotron 3.5 Lightning 30B-A3B30B · MMLU-Pro 81.6Q8_0 · ~74.8 t/s
- Muse Glimmer 30B27.8B · MMLU-Pro N/AQ8_0 · ~26.9 t/s
- Gemma 2 27B Instruct27.2B · MMLU-Pro 38.0Q8_0 · ~25 t/s
- Gemma 3 27B Instruct27B · MMLU-Pro 67.5Q8_0 · ~26.4 t/s
- Qwen 3.6 27B27B · MMLU-Pro 86.2Q8_0 · ~27.3 t/s
- UI-Mate 27B27B · MMLU-Pro ~86.2Q8_0 · ~27.3 t/s
- Bonsai 27B27B · MMLU-Pro 81.5Ternary (Q2_0) · ~90.7 t/s
- Qwen 3.8 27B27B · MMLU-Pro N/AQ8_0 · ~27.3 t/s
- Gemma 4 26B (MoE)25.2B · MMLU-Pro 82.6Q8_0 · ~57.7 t/s
- Mistral Small 3.1 24B Instruct24B · MMLU-Pro 66.8Q8_0 · ~29.7 t/s
- Mistral Small 22B22.2B · MMLU-Pro 49.2Q8_0 · ~31.4 t/s
- GPT-OSS 20B21B · MMLU-Pro 67.9Q8_0 · ~61.6 t/s
- Qwen3 14B14.8B · MMLU-Pro 61.0BF16 · ~25.8 t/s
- Qwen 2.5 14B Instruct14.7B · MMLU-Pro 63.7BF16 · ~25.8 t/s
- Phi-4 14B Instruct14B · MMLU-Pro 70.4BF16 · ~27.2 t/s
- Mistral Nemo 12B Instruct12.2B · MMLU-Pro 35.6BF16 · ~31 t/s
- Gemma 3 12B Instruct12.2B · MMLU-Pro 60.6BF16 · ~31.4 t/s
- Gemma 4 12B (Unified)12B · MMLU-Pro 77.2BF16 · ~29.3 t/s
- Gemma 2 9B Instruct9.2B · MMLU-Pro 32.0FP32 · ~20.2 t/s
- Qwen 3.5 9B9B · MMLU-Pro 82.5FP32 · ~22 t/s
- Ornith 1.5 9B9B · MMLU-Pro N/AFP32 · ~22 t/s
- Llama 3.1 8B Instruct8B · MMLU-Pro 48.3FP32 · ~24.2 t/s
- DeepSeek R1 Distill Llama 8B8B · MMLU-Pro 41.0FP32 · ~24.2 t/s
- Qwen3 8B8B · MMLU-Pro 56.7FP32 · ~24.1 t/s
- Qwen 2.5 7B Instruct7.6B · MMLU-Pro 56.3FP32 · ~25.9 t/s
- Mistral 7B Instruct v0.37.25B · MMLU-Pro 30.0FP32 · ~26.6 t/s
- Gemma 3 4B Instruct4B · MMLU-Pro 43.6FP32 · ~48.4 t/s
- Gemma 4 E4B4B · MMLU-Pro 69.4FP32 · ~47 t/s
- Phi-3.5 Mini Instruct3.8B · MMLU-Pro 47.4FP32 · ~43.4 t/s
- Phi-4-mini Instruct3.8B · MMLU-Pro 67.3FP32 · ~49.1 t/s
- Llama 3.2 3B Instruct3.2B · MMLU-Pro 24.0FP32 · ~58.1 t/s
- Qwen 2.5 3B Instruct3.1B · MMLU-Pro 32.4FP32 · ~62.9 t/s
- Gemma 2 2B Instruct2.6B · MMLU-Pro 17.8FP32 · ~70.9 t/s
- Gemma 4 E2B2B · MMLU-Pro 60.0FP32 · ~95.1 t/s
- SmolLM2 1.7B Instruct1.7B · MMLU-Pro 19.0FP32 · ~95 t/s
- Qwen 2.5 1.5B Instruct1.5B · MMLU-Pro 16.8FP32 · ~128.1 t/s
- Llama 3.2 1B Instruct1.24B · MMLU-Pro 12.5FP32 · ~152.8 t/s
- Gemma 3 1B Instruct1B · MMLU-Pro 14.7FP32 · ~184.6 t/s
- Qwen 2.5 0.5B Instruct0.5B · MMLU-Pro 10.0FP32 · ~380.3 t/s
- SmolLM2 360M Instruct0.36B · MMLU-Pro 8.0FP32 · ~449.9 t/s
Models that fit with CPU offload (8)
These use system RAM for layers that don't fit in VRAM, so expect much slower inference.
- Mixtral 8x22B Instruct v0.1141B · MMLU-Pro 40.0Q2_K · ~2.4 t/s
- Mistral Medium 3.5 128B128B · MMLU-Pro N/AQ2_K · ~3.3 t/s
- Qwen 3.5 122B-A10B (MoE)122B · MMLU-Pro 86.7Q3_K_M · ~5 t/s
- Nemotron 3 Super 120B120B · MMLU-Pro 83.7Q3_K_M · ~5.1 t/s
- GPT-OSS 120B117B · MMLU-Pro 80.7Q3_K_M · ~9.9 t/s
- Llama 4 Scout 109B109B · MMLU-Pro 74.3Q3_K_M · ~4.2 t/s
- GLM-4.5 Air 106B106B · MMLU-Pro 81.4Q3_K_M · ~7.6 t/s
- GLM-4.6V 106B106B · MMLU-Pro 79.9Q3_K_M · ~7.6 t/s
Too large for this GPU (29)
- Llama 3.1 405B Instruct
- DeepSeek V3 671B
- DeepSeek R1 671B
- Llama 4 Maverick 400B
- Qwen3 235B-A22B (MoE)
- MiniMax M1 456B
- GLM-4.5 355B
- GLM-4.6 355B
- GLM-4.7 358B
- MiniMax M2.5 229B
- GLM-5 744B
- MiniMax M2.7 229B
- Kimi K2.6
- GLM-5.1 754B
- DeepSeek V4 Pro 1.6T
- DeepSeek V4 Flash 284B
- 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
- DeepSeek V4 Pro 0813 1.6T
- Ornith 1.5 397B (MoE)
Frequently asked questions
- How much VRAM does the Intel Data Center GPU Max 1100 have?
- The Intel Data Center GPU Max 1100 has 48 GB of HBM2e with 1229 GB/s memory bandwidth.
- What is the Intel Data Center GPU Max 1100 best for?
- With 48 GB of VRAM, the Intel Data Center GPU Max 1100 is ideal for running 70B-class models at Q4 quantization and large MoE models, a workstation sweet spot for local inference.
- What LLMs can the Intel Data Center GPU Max 1100 run locally?
- The Intel Data Center GPU Max 1100 can run 57 of the 94 open-weight models tracked by CanItRun natively in VRAM at 8k context. Top options include: Qwen 3.8 27B at Q8_0, Muse Glimmer 30B at Q8_0, Ornith 1.5 9B at FP32.
- Can the Intel Data Center GPU Max 1100 run Gemma 4 31B?
- Yes. The Intel Data Center GPU Max 1100 runs Gemma 4 31B natively in VRAM at Q8_0 quantization, achieving approximately 23.4 tokens per second.
- Can the Intel Data Center GPU Max 1100 run Qwen 3.6 27B?
- Yes. The Intel Data Center GPU Max 1100 runs Qwen 3.6 27B natively in VRAM at Q8_0 quantization, achieving approximately 27.3 tokens per second.
- Can the Intel Data Center GPU Max 1100 run Qwen3 8B?
- Yes. The Intel Data Center GPU Max 1100 runs Qwen3 8B natively in VRAM at FP32 quantization, achieving approximately 24.1 tokens per second.
- Can I rent the Intel Data Center GPU Max 1100 instead of buying it?
- Yes: RunPod and similar cloud GPU providers let you rent Intel Data Center GPU Max 1100 instances by the hour, with no long-term contract. This is often cheaper than buying if you only need it occasionally, and lets you try the GPU before committing to a purchase.