DeepSeek · MIT · HF: deepseek-ai/DeepSeek-R1-Distill-Llama-70B
DeepSeek-R1 Distill Llama 70B — VRAM, KV Cache & Hardware Matrix
Serverless API Rate
$0.20 in / $0.60 out per 1M
Parameters
70.6B Dense
Attention
GQA (8 KV / 64 Q)
Layers × Hidden
80 × 8192
Max Native Context
128k tokens
MoE Experts
N/A (Dense)
Quantization Memory Footprint
| Quantization | BPW | Weights Only | Total @ 8k Ctx | Total @ 32k Ctx | Quality |
|---|---|---|---|---|---|
| FP16 | 16.00 | 134.13 GiB | 138.51 GiB | 146.01 GiB | Lossless Reference |
| Q8_0 | 8.50 | 71.26 GiB | 75.63 GiB | 83.13 GiB | Near-Lossless |
| Q6_K | 6.57 | 55.08 GiB | 59.45 GiB | 66.95 GiB | High Precision |
| Q5_K_M | 5.69 | 47.70 GiB | 52.08 GiB | 59.58 GiB | Balanced High |
| Q4_K_M | 4.85 | 40.66 GiB | 45.03 GiB | 52.53 GiB | Recommended 4-bit |
| MLX_4BIT | 4.50 | 37.72 GiB | 42.10 GiB | 49.60 GiB | Apple Silicon Optimized |
| IQ4_XS | 4.25 | 35.63 GiB | 40.00 GiB | 47.50 GiB | Compact 4-bit |
| Q3_K_M | 3.91 | 32.78 GiB | 37.15 GiB | 44.65 GiB | Aggressive Compression |
Hardware Compatibility & Speed Leaderboard (Q4_K_M @ 32k Context)
| Hardware | Status | Total / Limit | Decode | TTFT (4k) | Max Safe Ctx | Inspect |
|---|---|---|---|---|---|---|
| Cloud NVIDIA H200 SXM (141 GB) | COMFORTABLE | 52.5 / 140.4 GiB | 74.1 tok/s | 0.88s | 128k | Simulate → |
| Cloud NVIDIA H100 SXM (80 GB) | COMFORTABLE | 52.5 / 79.4 GiB | 51.7 tok/s | 0.88s | 118k | Simulate → |
| Cloud NVIDIA A100 SXM (80 GB) | COMFORTABLE | 52.5 / 79.4 GiB | 30.7 tok/s | 2.87s | 118k | Simulate → |
| NVIDIA RTX PRO 6000 Blackwell (96 GB) | COMFORTABLE | 52.5 / 95.0 GiB | 27.7 tok/s | 3.57s | 128k | Simulate → |
| Apple Mac Studio M3 Ultra (256 GB) | COMFORTABLE | 52.3 / 199.7 GiB | 11.7 tok/s | 17.88s | 128k | Simulate → |
| Apple Mac Studio M3 Ultra (512 GB) | COMFORTABLE | 52.3 / 399.4 GiB | 11.7 tok/s | 17.88s | 128k | Simulate → |
| Apple Mac Studio M2 Ultra (192 GB) | COMFORTABLE | 52.3 / 149.8 GiB | 11.5 tok/s | 19.74s | 128k | Simulate → |
| Apple Mac Studio M4 Max (128 GB) | COMFORTABLE | 52.3 / 99.8 GiB | 7.7 tok/s | 27.23s | 128k | Simulate → |
| Apple MacBook Pro M3 Max (128 GB) | COMFORTABLE | 52.3 / 99.8 GiB | 5.6 tok/s | 36.48s | 128k | Simulate → |
| Ryzen AI Max+ 395 Strix Halo (128 GB) | COMFORTABLE | 52.3 / 96.0 GiB | 3.5 tok/s | 20.25s | 128k | Simulate → |
| Apple Mac Studio M4 Max (64 GB) | TUNABLE_UMA | 52.3 / 49.9 GiB | 7.7 tok/s | 27.23s | 45k | Simulate → |
| Apple MacBook Pro M3 Max (64 GB) | TUNABLE_UMA | 52.3 / 49.9 GiB | 5.6 tok/s | 36.48s | 45k | Simulate → |
| Apple Mac Mini M4 Pro (64 GB) | TUNABLE_UMA | 52.3 / 49.9 GiB | 3.7 tok/s | 56.29s | 45k | Simulate → |
| Ryzen AI Max+ 395 Strix Halo (64 GB) | TUNABLE_UMA | 52.3 / 48.0 GiB | 3.5 tok/s | 20.25s | 37k | Simulate → |
| Ryzen AI 9 HX 370 Mini-PC (64 GB) | TUNABLE_UMA | 52.3 / 48.0 GiB | 1.5 tok/s | 58.04s | 37k | Simulate → |
| NVIDIA RTX 5090 (32 GB) | OOM | 52.5 / 31.0 GiB | OOM | — | OOM | Simulate → |
| NVIDIA RTX 4090 (24 GB) | OOM | 52.5 / 23.0 GiB | OOM | — | OOM | Simulate → |
| NVIDIA RTX 5080 (16 GB) | OOM | 52.5 / 15.0 GiB | OOM | — | OOM | Simulate → |
| NVIDIA RTX 3090 (24 GB Used) | OOM | 52.5 / 23.0 GiB | OOM | — | OOM | Simulate → |
| NVIDIA RTX 5070 Ti (16 GB) | OOM | 52.5 / 15.0 GiB | OOM | — | OOM | Simulate → |
| AMD Radeon RX 7900 XTX (24 GB) | OOM | 52.5 / 23.0 GiB | OOM | — | OOM | Simulate → |
| Cloud NVIDIA L40S (48 GB) | OOM | 52.5 / 47.4 GiB | OOM | — | 16k | Simulate → |
| 2x NVIDIA RTX 3090 Rig (48 GB) | OOM | 52.5 / 46.6 GiB | OOM | — | 13k | Simulate → |
| NVIDIA RTX 5060 Ti (16 GB) | OOM | 52.5 / 15.0 GiB | OOM | — | OOM | Simulate → |
| NVIDIA RTX 3060 (12 GB) | OOM | 52.5 / 11.0 GiB | OOM | — | OOM | Simulate → |
| Apple Mac Mini M4 Pro (48 GB) | OOM | 52.3 / 37.4 GiB | OOM | — | 2k | Simulate → |
| Apple Mac Mini M4 (16 GB) | OOM | 52.3 / 10.0 GiB | OOM | — | OOM | Simulate → |
| Apple Mac Mini M4 (24 GB) | OOM | 52.3 / 18.0 GiB | OOM | — | OOM | Simulate → |
Interactive DeepSeek-R1 Distill Llama 70B Simulator
70.6B Dense · GQA
1792 GB/s · 250 TFLOPS
3. Weight Quantization
4.85 bpw
4. KV Cache
32,768 tok
Compute Roofline
COMFORTABLE FIT Memory-Bandwidth Bound
Max Safe Ctx: 128k
Memory Allocation Footprint 52.53 / 95.00 GiB usable
Weights: 40.66 GiB
KV Cache: 10.00 GiB
Overhead: 1.88 GiB
Headroom: 42.47 GiB
Decode Speed
27.7
tok/s (batch=1)
Prefill Speed
1160
prompt tok/s
TTFT Latency
3.57s
for 4,096 tok
Active Params
70.6B
of 70.6B total
llama.cpp Server Command
llama-server -hf deepseek-ai/DeepSeek-R1-Distill-Llama-70B:Q4_K_M -ngl 99 -fa on -c 32768 --port 8080| Context | KV Cache | Total VRAM | Decode | Status |
|---|---|---|---|---|
| 4k | 1.25 GiB | 43.22 GiB | 33.5 tok/s | COMFORTABLE |
| 8k | 2.50 GiB | 45.03 GiB | 32.5 tok/s | COMFORTABLE |
| 16k | 5.00 GiB | 47.53 GiB | 30.7 tok/s | COMFORTABLE |
| 32k | 10.00 GiB | 52.53 GiB | 27.7 tok/s | COMFORTABLE |
| 64k | 20.00 GiB | 62.53 GiB | 23.1 tok/s | COMFORTABLE |
| 128k | 40.00 GiB | 82.53 GiB | 17.4 tok/s | COMFORTABLE |
Buy-vs-Cloud-vs-API Monthly TCO Break-Even
USE SERVERLESS APIOwn Hardware/mo
$175.61
162.6h active + power
Cloud GPU/mo
$345.84
@$1.85/hr spot/pod
Serverless API/mo
$19.00
API cheaper
Recommended Hardware & Cloud Deployment
Contextual to fit statusEngineering FAQ — DeepSeek-R1 Distill Llama 70B
How much VRAM does DeepSeek-R1 Distill Llama 70B require in Q4_K_M?
DeepSeek-R1 Distill Llama 70B weights occupy 40.66 GiB in Q4_K_M. Including 32k FP16 KV cache and runtime buffers, total memory is 52.53 GiB.
What is the cheapest local hardware that runs DeepSeek-R1 Distill Llama 70B?
NVIDIA RTX PRO 6000 Blackwell (96 GB) (96 GB) runs DeepSeek-R1 Distill Llama 70B Q4_K_M at ~27.7 tok/s decode and 3.57s TTFT.
How does DeepSeek-R1 Distill Llama 70B scale KV cache at long context?
DeepSeek-R1 Distill Llama 70B uses GQA with 8 KV heads across 80 layers (128 head_dim).