Saiga Llama3 8B — Hardware Requirements & GPU Compatibility
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Saiga Llama3 8B is a 8.0B-parameter open language model from IlyaGusev in the Llama 3 family. It supports a context window of up to 8,192 tokens. At Q4_K_M it needs about 5.39 GB of VRAM — see which GPUs and Macs can run it below.
407.0K downloads
141 likesSpecifications
- Publisher
- IlyaGusev
- Family
- Llama 3
- Parameters
- 8.0B
- Architecture
- LlamaForCausalLM
- Context Length
- 8,192 tokens
- Vocabulary Size
- 128,256
- Release Date
- 2024-07-04
- License
- Other
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HuggingFace
How Much VRAM Does Saiga Llama3 8B Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_K | 3.40 | 4.0 GB | 4.8 GB | 3.41 GB | 2-bit quantization with K-quant improvements |
| Q3_K_S | 3.50 | 4.1 GB | 4.9 GB | 3.51 GB | 3-bit small quantization |
| Q3_K_M | 3.90 | 4.5 GB | 5.3 GB | 3.91 GB | 3-bit medium quantization |
| Q4_0 | 4.00 | 4.6 GB | 5.4 GB | 4.02 GB | 4-bit legacy quantization |
| Q4_K_M | 4.80 | 5.4 GB | 6.2 GB | 4.82 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_M | 5.70 | 6.3 GB | 7.1 GB | 5.72 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_K | 6.60 | 7.2 GB | 8 GB | 6.62 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 8.6 GB | 9.4 GB | 8.03 GB | 8-bit quantization, near-lossless |
Which GPUs Can Run Saiga Llama3 8B?
Q4_K_M · 5.4 GBSaiga Llama3 8B (Q4_K_M) requires 5.4 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 8K context window can add up to 0.8 GB, bringing total usage to 6.2 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 Saiga Llama3 8B?
Q4_K_M · 5.4 GB33 devices with unified memory can run Saiga Llama3 8B, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, MacBook Air 13" M3 (8 GB).
Runs great
— Plenty of headroomDecent
— Enough memory, may be tightRelated Models
Frequently Asked Questions
- How much VRAM does Saiga Llama3 8B need?
Saiga Llama3 8B requires 5.4 GB of VRAM at Q4_K_M, or 8.6 GB at Q8_0. Full 8K context adds up to 0.8 GB (6.2 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 8.0B × 4.8 bits ÷ 8 = 4.8 GB
KV Cache + Overhead ≈ 0.6 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 1.4 GB (at full 8K context)
VRAM usage by quantization
Q4_K_M5.4 GBQ4_K_M + full context6.2 GB- What's the best quantization for Saiga Llama3 8B?
For Saiga Llama3 8B, Q4_K_M (5.4 GB) offers the best balance of quality and VRAM usage. Q5_0 (5.6 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 4.0 GB.
VRAM requirement by quantization
Q2_K4.0 GBQ3_K_L4.7 GBQ4_K_S5.1 GBQ4_K_M ★5.4 GBQ5_K_S6.1 GBQ8_08.6 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run Saiga Llama3 8B on a Mac?
Saiga Llama3 8B requires at least 4.0 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 Saiga Llama3 8B locally?
Yes — Saiga Llama3 8B can run locally on consumer hardware. At Q4_K_M quantization it needs 5.4 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Saiga Llama3 8B?
At Q4_K_M, Saiga Llama3 8B can reach ~541 tok/s on AMD Instinct MI300X. On NVIDIA GeForce RTX 4090: ~122 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 ÷ 5.4 × 0.55 = ~541 tok/s
Estimated speed at Q4_K_M (5.4 GB)
~541 tok/s~122 tok/s~404 tok/s~334 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Saiga Llama3 8B?
At Q4_K_M, the download is about 4.82 GB. The full-precision Q8_0 version is 8.03 GB. The smallest option (Q2_K) is 3.41 GB.
- Which GPUs can run Saiga Llama3 8B?
35 consumer GPUs can run Saiga Llama3 8B at Q4_K_M (5.4 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 Saiga Llama3 8B?
33 devices with unified memory can run Saiga Llama3 8B at Q4_K_M (5.4 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.