Llemma 7B — Hardware Requirements & GPU Compatibility
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Llemma 7B is a 7B-parameter open language model from EleutherAI. It supports a context window of up to 4,096 tokens. At Q4_K_M it needs about 5.57 GB of VRAM — see which GPUs and Macs can run it below.
631 downloads
112 likesSpecifications
- Publisher
- EleutherAI
- Parameters
- 7B
- Architecture
- LlamaForCausalLM
- Context Length
- 4,096 tokens
- Vocabulary Size
- 32,016
- Release Date
- 2023-09-12
- License
- Llama 2 Community
Get Started
HuggingFace
How Much VRAM Does Llemma 7B Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_Kest. | 3.40 | 4.3 GB | 5.4 GB | 2.98 GB | 2-bit quantization with K-quant improvements |
| Q3_K_Mest. | 3.90 | 4.8 GB | 5.9 GB | 3.41 GB | 3-bit medium quantization |
| Q4_K_Mest. | 4.80 | 5.6 GB | 6.7 GB | 4.20 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_Mest. | 5.70 | 6.4 GB | 7.4 GB | 4.99 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_Kest. | 6.60 | 7.2 GB | 8.2 GB | 5.78 GB | 6-bit quantization, very good quality |
| Q8_0est. | 8.00 | 8.4 GB | 9.4 GB | 7.00 GB | 8-bit quantization, near-lossless |
| BF16est. | 16.00 | 15.4 GB | 16.4 GB | 14.00 GB | Brain floating point 16 — preferred for training |
est.= calculated VRAM estimate; no published GGUF file found for that quantization yet. Other rows are verified against real community uploads.
Which GPUs Can Run Llemma 7B?
Q4_K_M · 5.6 GBLlemma 7B (Q4_K_M) requires 5.6 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 8+ GB is recommended. Using the full 4K context window can add up to 1.1 GB, bringing total usage to 6.7 GB. 35 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti, NVIDIA GeForce RTX 3070 Ti.
Runs great
— Plenty of headroom
Which Devices Can Run Llemma 7B?
Q4_K_M · 5.6 GB33 devices with unified memory can run Llemma 7B, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, MacBook Air 13" M3 (8 GB).
Runs great
— Plenty of headroomDecent
— Enough memory, may be tightBenchmarks
Benchmark details →Related Models
Frequently Asked Questions
- How much VRAM does Llemma 7B need?
Llemma 7B requires 5.6 GB of VRAM at Q4_K_M, or 15.4 GB at BF16. Full 4K context adds up to 1.1 GB (6.7 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 7B × 4.8 bits ÷ 8 = 4.2 GB
KV Cache + Overhead ≈ 1.4 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 2.5 GB (at full 4K context)
VRAM usage by quantization
Q4_K_M5.6 GBQ4_K_M + full context6.7 GB- What's the best quantization for Llemma 7B?
For Llemma 7B, Q4_K_M (5.6 GB) offers the best balance of quality and VRAM usage. Q5_K_M (6.4 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 4.3 GB.
VRAM requirement by quantization
Q2_K4.3 GBQ4_K_M ★5.6 GBQ5_K_M6.4 GBQ6_K7.2 GBQ8_08.4 GBBF1615.4 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run Llemma 7B on a Mac?
Llemma 7B requires at least 4.3 GB at Q2_K, which exceeds the unified memory of most consumer Macs. You would need a Mac Studio or Mac Pro with a high-memory configuration.
- Can I run Llemma 7B locally?
Yes — Llemma 7B can run locally on consumer hardware. At Q4_K_M quantization it needs 5.6 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Llemma 7B?
At Q4_K_M, Llemma 7B can reach ~523 tok/s on AMD Instinct MI300X. On NVIDIA GeForce RTX 4090: ~118 tok/s. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.
tok/s = (bandwidth GB/s ÷ model GB) × efficiency
Example: AMD Instinct MI300X → 5300 ÷ 5.6 × 0.55 = ~523 tok/s
Estimated speed at Q4_K_M (5.6 GB)
~523 tok/s~118 tok/s~391 tok/s~324 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Llemma 7B?
At Q4_K_M, the download is about 4.20 GB. The full-precision BF16 version is 14.00 GB. The smallest option (Q2_K) is 2.98 GB.
- Which GPUs can run Llemma 7B?
35 consumer GPUs can run Llemma 7B at Q4_K_M (5.6 GB). Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT, AMD Radeon RX 7600. 28 GPUs have plenty of headroom for comfortable inference.
- Which devices can run Llemma 7B?
33 devices with unified memory can run Llemma 7B at Q4_K_M (5.6 GB), including Mac Mini M4 (16 GB), Mac Mini M4 (32 GB), Mac Mini M4 Pro (24 GB), Mac Mini M4 Pro (48 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.