Natsumura Storytelling Rp 1.0 Llama 3.1 8B — Hardware Requirements & GPU Compatibility
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Natsumura Storytelling Rp 1.0 Llama 3.1 8B is a 8B-parameter open language model from tohur in the Llama 3 family. It supports a context window of up to 131,072 tokens. At Q4_K_M it needs about 5.37 GB of VRAM — see which GPUs and Macs can run it below.
29 downloads
8 likesSpecifications
- Publisher
- tohur
- Family
- Llama 3
- Parameters
- 8B
- Architecture
- LlamaForCausalLM
- Context Length
- 131,072 tokens
- Vocabulary Size
- 128,256
- Release Date
- 2024-07-26
- License
- Llama 3.1 Community
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How Much VRAM Does Natsumura Storytelling Rp 1.0 Llama 3.1 8B Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_Kest. | 3.40 | 4.0 GB | 20.9 GB | 3.40 GB | 2-bit quantization with K-quant improvements |
| Q3_K_Mest. | 3.90 | 4.5 GB | 21.4 GB | 3.90 GB | 3-bit medium quantization |
| Q4_K_Mest. | 4.80 | 5.4 GB | 22.3 GB | 4.80 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_Mest. | 5.70 | 6.3 GB | 23.2 GB | 5.70 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_Kest. | 6.60 | 7.2 GB | 24.1 GB | 6.60 GB | 6-bit quantization, very good quality |
| Q8_0est. | 8.00 | 8.6 GB | 25.5 GB | 8.00 GB | 8-bit quantization, near-lossless |
| BF16est. | 16.00 | 16.6 GB | 33.5 GB | 16.00 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 Natsumura Storytelling Rp 1.0 Llama 3.1 8B?
Q4_K_M · 5.4 GBNatsumura Storytelling Rp 1.0 Llama 3.1 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, 7+ GB is recommended. Using the full 131K context window can add up to 16.9 GB, bringing total usage to 22.3 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 Natsumura Storytelling Rp 1.0 Llama 3.1 8B?
Q4_K_M · 5.4 GB33 devices with unified memory can run Natsumura Storytelling Rp 1.0 Llama 3.1 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 Natsumura Storytelling Rp 1.0 Llama 3.1 8B need?
Natsumura Storytelling Rp 1.0 Llama 3.1 8B requires 5.4 GB of VRAM at Q4_K_M, or 16.6 GB at BF16. Full 131K context adds up to 16.9 GB (22.3 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 8B × 4.8 bits ÷ 8 = 4.8 GB
KV Cache + Overhead ≈ 0.6 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 17.5 GB (at full 131K context)
VRAM usage by quantization
Q4_K_M5.4 GBQ4_K_M + full context22.3 GB- What's the best quantization for Natsumura Storytelling Rp 1.0 Llama 3.1 8B?
For Natsumura Storytelling Rp 1.0 Llama 3.1 8B, 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 Natsumura Storytelling Rp 1.0 Llama 3.1 8B on a Mac?
Natsumura Storytelling Rp 1.0 Llama 3.1 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 Natsumura Storytelling Rp 1.0 Llama 3.1 8B locally?
Yes — Natsumura Storytelling Rp 1.0 Llama 3.1 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 Natsumura Storytelling Rp 1.0 Llama 3.1 8B?
At Q4_K_M, Natsumura Storytelling Rp 1.0 Llama 3.1 8B can reach ~543 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 = ~543 tok/s
Estimated speed at Q4_K_M (5.4 GB)
~543 tok/s~122 tok/s~406 tok/s~336 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Natsumura Storytelling Rp 1.0 Llama 3.1 8B?
At Q4_K_M, the download is about 4.80 GB. The full-precision BF16 version is 16.00 GB. The smallest option (Q2_K) is 3.40 GB.
- Which GPUs can run Natsumura Storytelling Rp 1.0 Llama 3.1 8B?
35 consumer GPUs can run Natsumura Storytelling Rp 1.0 Llama 3.1 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 Natsumura Storytelling Rp 1.0 Llama 3.1 8B?
33 devices with unified memory can run Natsumura Storytelling Rp 1.0 Llama 3.1 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.