DeepSeek R1 Distill Llama 70B Quantized.w4a16 — Hardware Requirements & GPU Compatibility
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4.6K downloads
6 likesSpecifications
- Publisher
- RedHatAI
- Family
- Llama
- Parameters
- 11.2B
- Architecture
- LlamaForCausalLM
- Context Length
- 131,072 tokens
- Vocabulary Size
- 128,256
- Release Date
- 2025-02-27
- License
- MIT
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How Much VRAM Does DeepSeek R1 Distill Llama 70B Quantized.w4a16 Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_K | 3.40 | 5.7 GB | 48.0 GB | 4.76 GB | 2-bit quantization with K-quant improvements |
| Q3_K_S | 3.50 | 5.9 GB | 48.1 GB | 4.90 GB | 3-bit small quantization |
| Q3_K_M | 3.90 | 6.4 GB | 48.7 GB | 5.46 GB | 3-bit medium quantization |
| Q4_0 | 4.00 | 6.6 GB | 48.9 GB | 5.60 GB | 4-bit legacy quantization |
| Q4_K_M | 4.80 | 7.7 GB | 50.0 GB | 6.72 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_M | 5.70 | 8.9 GB | 51.2 GB | 7.98 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_K | 6.60 | 10.2 GB | 52.5 GB | 9.24 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 12.2 GB | 54.5 GB | 11.20 GB | 8-bit quantization, near-lossless |
Which GPUs Can Run DeepSeek R1 Distill Llama 70B Quantized.w4a16?
Q4_K_M · 7.7 GBDeepSeek R1 Distill Llama 70B Quantized.w4a16 (Q4_K_M) requires 7.7 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 10+ GB is recommended. Using the full 131K context window can add up to 42.3 GB, bringing total usage to 50.0 GB. 35 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti, NVIDIA GeForce RTX 3080.
Runs great
— Plenty of headroom
Decent
— Enough VRAM, may be tightWhich Devices Can Run DeepSeek R1 Distill Llama 70B Quantized.w4a16?
Q4_K_M · 7.7 GB33 devices with unified memory can run DeepSeek R1 Distill Llama 70B Quantized.w4a16, 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 DeepSeek R1 Distill Llama 70B Quantized.w4a16 need?
DeepSeek R1 Distill Llama 70B Quantized.w4a16 requires 7.7 GB of VRAM at Q4_K_M, or 12.2 GB at Q8_0. Full 131K context adds up to 42.3 GB (50.0 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 11.2B × 4.8 bits ÷ 8 = 6.7 GB
KV Cache + Overhead ≈ 1 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 43.3 GB (at full 131K context)
VRAM usage by quantization
Q4_K_M7.7 GBQ4_K_M + full context50.0 GB- What's the best quantization for DeepSeek R1 Distill Llama 70B Quantized.w4a16?
For DeepSeek R1 Distill Llama 70B Quantized.w4a16, Q4_K_M (7.7 GB) offers the best balance of quality and VRAM usage. Q5_K_S (8.7 GB) provides better quality if you have the VRAM. The smallest option is IQ2_XXS at 4.0 GB.
VRAM requirement by quantization
IQ2_XXS4.0 GB~53%Q2_K5.7 GB~75%Q3_K_L6.7 GB~86%IQ4_NL7.3 GB~88%Q4_K_M ★7.7 GB~89%Q8_012.2 GB~99%★ Recommended — best balance of quality and VRAM usage.
- Can I run DeepSeek R1 Distill Llama 70B Quantized.w4a16 on a Mac?
DeepSeek R1 Distill Llama 70B Quantized.w4a16 requires at least 4.0 GB at IQ2_XXS, 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 DeepSeek R1 Distill Llama 70B Quantized.w4a16 locally?
Yes — DeepSeek R1 Distill Llama 70B Quantized.w4a16 can run locally on consumer hardware. At Q4_K_M quantization it needs 7.7 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is DeepSeek R1 Distill Llama 70B Quantized.w4a16?
At Q4_K_M, DeepSeek R1 Distill Llama 70B Quantized.w4a16 can reach ~379 tok/s on AMD Instinct MI300X. On NVIDIA GeForce RTX 4090: ~85 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 ÷ 7.7 × 0.55 = ~379 tok/s
Estimated speed at Q4_K_M (7.7 GB)
AMD Instinct MI300X~379 tok/sNVIDIA GeForce RTX 4090~85 tok/sNVIDIA H100 SXM~283 tok/sAMD Instinct MI250X~234 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of DeepSeek R1 Distill Llama 70B Quantized.w4a16?
At Q4_K_M, the download is about 6.72 GB. The full-precision Q8_0 version is 11.20 GB. The smallest option (IQ2_XXS) is 3.08 GB.
- Which GPUs can run DeepSeek R1 Distill Llama 70B Quantized.w4a16?
35 consumer GPUs can run DeepSeek R1 Distill Llama 70B Quantized.w4a16 at Q4_K_M (7.7 GB). Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT, AMD Radeon RX 7600. 26 GPUs have plenty of headroom for comfortable inference.
- Which devices can run DeepSeek R1 Distill Llama 70B Quantized.w4a16?
33 devices with unified memory can run DeepSeek R1 Distill Llama 70B Quantized.w4a16 at Q4_K_M (7.7 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.