Llama 3 8B Instruct Gradient 1048k — Hardware Requirements & GPU Compatibility
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Llama 3 8B Instruct Gradient 1048k is a 8.0B-parameter open language model from gradientai in the Llama 3 family. It supports a context window of up to 1,048,576 tokens. At Q4_K_M it needs about 5.39 GB of VRAM — see which GPUs and Macs can run it below.
43.5K downloads
683 likesSpecifications
- Publisher
- gradientai
- Family
- Llama 3
- Parameters
- 8.0B
- Architecture
- LlamaForCausalLM
- Context Length
- 1,048,576 tokens
- Vocabulary Size
- 128,256
- Release Date
- 2024-04-29
- License
- Llama 3 Community
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How Much VRAM Does Llama 3 8B Instruct Gradient 1048k Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_Kest. | 3.40 | 4.0 GB | 141.2 GB | 3.41 GB | 2-bit quantization with K-quant improvements |
| Q3_K_Mest. | 3.90 | 4.5 GB | 141.7 GB | 3.91 GB | 3-bit medium quantization |
| Q4_K_Mest. | 4.80 | 5.4 GB | 142.6 GB | 4.82 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_Mest. | 5.70 | 6.3 GB | 143.5 GB | 5.72 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_Kest. | 6.60 | 7.2 GB | 144.4 GB | 6.62 GB | 6-bit quantization, very good quality |
| Q8_0est. | 8.00 | 8.6 GB | 145.8 GB | 8.03 GB | 8-bit quantization, near-lossless |
| BF16est. | 16.00 | 16.6 GB | 153.8 GB | 16.06 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 Llama 3 8B Instruct Gradient 1048k?
Q4_K_M · 5.4 GBLlama 3 8B Instruct Gradient 1048k (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 1049K context window can add up to 137.2 GB, bringing total usage to 142.6 GB. 52 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti, NVIDIA GeForce RTX 3070 Ti.
Runs great
— Plenty of headroom
Decent
— Enough VRAM, may be tightWhich Devices Can Run Llama 3 8B Instruct Gradient 1048k?
Q4_K_M · 5.4 GB58 devices with unified memory can run Llama 3 8B Instruct Gradient 1048k, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, MacBook Air 13" M3 (8 GB).
Runs great
— Plenty of headroomRelated Models
Frequently Asked Questions
- How much VRAM does Llama 3 8B Instruct Gradient 1048k need?
Llama 3 8B Instruct Gradient 1048k requires 5.4 GB of VRAM at Q4_K_M, or 16.6 GB at BF16. Full 1049K context adds up to 137.2 GB (142.6 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 ≈ 137.8 GB (at full 1049K context)
VRAM usage by quantization
Q4_K_M5.4 GBQ4_K_M + full context142.6 GB- What's the best quantization for Llama 3 8B Instruct Gradient 1048k?
For Llama 3 8B Instruct Gradient 1048k, Q4_K_M (5.4 GB) offers the best balance of quality and VRAM usage. Q5_K_M (6.3 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 GBQ4_K_M ★5.4 GBQ5_K_M6.3 GBQ6_K7.2 GBQ8_08.6 GBBF1616.6 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run Llama 3 8B Instruct Gradient 1048k on a Mac?
Llama 3 8B Instruct Gradient 1048k 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 Llama 3 8B Instruct Gradient 1048k locally?
Yes — Llama 3 8B Instruct Gradient 1048k 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 Llama 3 8B Instruct Gradient 1048k?
At Q4_K_M, Llama 3 8B Instruct Gradient 1048k can reach ~891 tok/s on AMD Instinct MI350X. 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: NVIDIA B200 → 8000 ÷ 5.4 × 0.65 = ~965 tok/s
Estimated speed at Q4_K_M (5.4 GB)
~965 tok/s~122 tok/s~965 tok/s~891 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Llama 3 8B Instruct Gradient 1048k?
At Q4_K_M, the download is about 4.82 GB. The full-precision BF16 version is 16.06 GB. The smallest option (Q2_K) is 3.41 GB.
- Which GPUs can run Llama 3 8B Instruct Gradient 1048k?
52 consumer GPUs can run Llama 3 8B Instruct Gradient 1048k 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. 40 GPUs have plenty of headroom for comfortable inference.
- Which devices can run Llama 3 8B Instruct Gradient 1048k?
59 devices with unified memory can run Llama 3 8B Instruct Gradient 1048k at Q4_K_M (5.4 GB), including AMD Ryzen AI 9 HX 370 (Strix Point) Laptop, ASUS Ascent GX10, Apple iPhone 17 Pro, Asus ROG Flow Z13 (2025, Ryzen AI Max+ 395, 128 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.