Qwen2.5 · Qwen · HF: Qwen/Qwen2.5-72B-Instruct
Qwen2.5 72B Instruct — VRAM, KV Cache & Hardware Matrix
Serverless API Rate
$0.36 in / $0.40 out per 1M
Parameters
72.7B Dense
Attention
GQA (8 KV / 64 Q)
Layers × Hidden
80 × 8192
Max Native Context
32k tokens
MoE Experts
N/A (Dense)
Quantization Memory Footprint
| Quantization | BPW | Weights Only | Total @ 8k Ctx | Total @ 32k Ctx | Quality |
|---|---|---|---|---|---|
| FP16 | 16.00 | 138.12 GiB | 142.50 GiB | 150.00 GiB | Lossless Reference |
| Q8_0 | 8.50 | 73.38 GiB | 77.75 GiB | 85.25 GiB | Near-Lossless |
| Q6_K | 6.57 | 56.72 GiB | 61.09 GiB | 68.59 GiB | High Precision |
| Q5_K_M | 5.69 | 49.12 GiB | 53.49 GiB | 60.99 GiB | Balanced High |
| Q4_K_M | 4.85 | 41.87 GiB | 46.24 GiB | 53.74 GiB | Recommended 4-bit |
| MLX_4BIT | 4.50 | 38.85 GiB | 43.22 GiB | 50.72 GiB | Apple Silicon Optimized |
| IQ4_XS | 4.25 | 36.69 GiB | 41.06 GiB | 48.56 GiB | Compact 4-bit |
| Q3_K_M | 3.91 | 33.75 GiB | 38.13 GiB | 45.63 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 | 53.7 / 140.4 GiB | 72.4 tok/s | 0.90s | 32k | Simulate → |
| Cloud NVIDIA H100 SXM (80 GB) | COMFORTABLE | 53.7 / 79.4 GiB | 50.5 tok/s | 0.90s | 32k | Simulate → |
| Cloud NVIDIA A100 SXM (80 GB) | COMFORTABLE | 53.7 / 79.4 GiB | 30.0 tok/s | 2.95s | 32k | Simulate → |
| NVIDIA RTX PRO 6000 Blackwell (96 GB) | COMFORTABLE | 53.7 / 95.0 GiB | 27.0 tok/s | 3.67s | 32k | Simulate → |
| Apple Mac Studio M3 Ultra (256 GB) | COMFORTABLE | 53.5 / 199.7 GiB | 11.5 tok/s | 18.39s | 32k | Simulate → |
| Apple Mac Studio M3 Ultra (512 GB) | COMFORTABLE | 53.5 / 399.4 GiB | 11.5 tok/s | 18.39s | 32k | Simulate → |
| Apple Mac Studio M2 Ultra (192 GB) | COMFORTABLE | 53.5 / 149.8 GiB | 11.2 tok/s | 20.31s | 32k | Simulate → |
| Apple Mac Studio M4 Max (128 GB) | COMFORTABLE | 53.5 / 99.8 GiB | 7.5 tok/s | 28.00s | 32k | Simulate → |
| Apple MacBook Pro M3 Max (128 GB) | COMFORTABLE | 53.5 / 99.8 GiB | 5.5 tok/s | 37.53s | 32k | Simulate → |
| Ryzen AI Max+ 395 Strix Halo (128 GB) | COMFORTABLE | 53.5 / 96.0 GiB | 3.4 tok/s | 20.83s | 32k | Simulate → |
| Apple Mac Studio M4 Max (64 GB) | TUNABLE_UMA | 53.5 / 49.9 GiB | 7.5 tok/s | 28.00s | 32k | Simulate → |
| Apple MacBook Pro M3 Max (64 GB) | TUNABLE_UMA | 53.5 / 49.9 GiB | 5.5 tok/s | 37.53s | 32k | Simulate → |
| Apple Mac Mini M4 Pro (64 GB) | TUNABLE_UMA | 53.5 / 49.9 GiB | 3.6 tok/s | 57.90s | 32k | Simulate → |
| Ryzen AI Max+ 395 Strix Halo (64 GB) | TUNABLE_UMA | 53.5 / 48.0 GiB | 3.4 tok/s | 20.83s | 32k | Simulate → |
| Ryzen AI 9 HX 370 Mini-PC (64 GB) | TUNABLE_UMA | 53.5 / 48.0 GiB | 1.5 tok/s | 59.70s | 32k | Simulate → |
| NVIDIA RTX 5090 (32 GB) | OOM | 53.7 / 31.0 GiB | OOM | — | OOM | Simulate → |
| NVIDIA RTX 4090 (24 GB) | OOM | 53.7 / 23.0 GiB | OOM | — | OOM | Simulate → |
| NVIDIA RTX 5080 (16 GB) | OOM | 53.7 / 15.0 GiB | OOM | — | OOM | Simulate → |
| NVIDIA RTX 3090 (24 GB Used) | OOM | 53.7 / 23.0 GiB | OOM | — | OOM | Simulate → |
| NVIDIA RTX 5070 Ti (16 GB) | OOM | 53.7 / 15.0 GiB | OOM | — | OOM | Simulate → |
| AMD Radeon RX 7900 XTX (24 GB) | OOM | 53.7 / 23.0 GiB | OOM | — | OOM | Simulate → |
| Cloud NVIDIA L40S (48 GB) | OOM | 53.7 / 47.4 GiB | OOM | — | 12k | Simulate → |
| 2x NVIDIA RTX 3090 Rig (48 GB) | OOM | 53.7 / 46.6 GiB | OOM | — | 9k | Simulate → |
| NVIDIA RTX 5060 Ti (16 GB) | OOM | 53.7 / 15.0 GiB | OOM | — | OOM | Simulate → |
| NVIDIA RTX 3060 (12 GB) | OOM | 53.7 / 11.0 GiB | OOM | — | OOM | Simulate → |
| Apple Mac Mini M4 Pro (48 GB) | OOM | 53.5 / 37.4 GiB | OOM | — | OOM | Simulate → |
| Apple Mac Mini M4 (16 GB) | OOM | 53.5 / 10.0 GiB | OOM | — | OOM | Simulate → |
| Apple Mac Mini M4 (24 GB) | OOM | 53.5 / 18.0 GiB | OOM | — | OOM | Simulate → |
Interactive Qwen2.5 72B Instruct Simulator
72.7B 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: 32k
Memory Allocation Footprint 53.74 / 95.00 GiB usable
Weights: 41.87 GiB
KV Cache: 10.00 GiB
Overhead: 1.88 GiB
Headroom: 41.26 GiB
Decode Speed
27.0
tok/s (batch=1)
Prefill Speed
1128
prompt tok/s
TTFT Latency
3.67s
for 4,096 tok
Active Params
72.7B
of 72.7B total
llama.cpp Server Command
llama-server -hf Qwen/Qwen2.5-72B-Instruct:Q4_K_M -ngl 99 -fa on -c 32768 --port 8080| Context | KV Cache | Total VRAM | Decode | Status |
|---|---|---|---|---|
| 4k | 1.25 GiB | 44.43 GiB | 32.5 tok/s | COMFORTABLE |
| 8k | 2.50 GiB | 46.24 GiB | 31.6 tok/s | COMFORTABLE |
| 16k | 5.00 GiB | 48.74 GiB | 29.9 tok/s | COMFORTABLE |
| 32k | 10.00 GiB | 53.74 GiB | 27.0 tok/s | COMFORTABLE |
Buy-vs-Cloud-vs-API Monthly TCO Break-Even
USE SERVERLESS APIOwn Hardware/mo
$176.00
166.5h active + power
Cloud GPU/mo
$354.16
@$1.85/hr spot/pod
Serverless API/mo
$24.00
Payback: 3791.9 mo
Recommended Hardware & Cloud Deployment
Contextual to fit statusEngineering FAQ — Qwen2.5 72B Instruct
How much VRAM does Qwen2.5 72B Instruct require in Q4_K_M?
Qwen2.5 72B Instruct weights occupy 41.87 GiB in Q4_K_M. Including 32k FP16 KV cache and runtime buffers, total memory is 53.74 GiB.
What is the cheapest local hardware that runs Qwen2.5 72B Instruct?
NVIDIA RTX PRO 6000 Blackwell (96 GB) (96 GB) runs Qwen2.5 72B Instruct Q4_K_M at ~27.0 tok/s decode and 3.67s TTFT.
How does Qwen2.5 72B Instruct scale KV cache at long context?
Qwen2.5 72B Instruct uses GQA with 8 KV heads across 80 layers (128 head_dim).