Qwen2.5 · Apache-2.0 · HF: Qwen/Qwen2.5-7B-Instruct
Qwen2.5 7B Instruct — VRAM, KV Cache & Hardware Matrix
Serverless API Rate
$0.04 in / $0.12 out per 1M
Parameters
7.6B Dense
Attention
GQA (4 KV / 28 Q)
Layers × Hidden
28 × 3584
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 | 14.44 GiB | 16.12 GiB | 17.43 GiB | Lossless Reference |
| Q8_0 | 8.50 | 7.67 GiB | 9.35 GiB | 10.66 GiB | Near-Lossless |
| Q6_K | 6.57 | 5.93 GiB | 7.61 GiB | 8.92 GiB | High Precision |
| Q5_K_M | 5.69 | 5.13 GiB | 6.81 GiB | 8.13 GiB | Balanced High |
| Q4_K_M | 4.85 | 4.38 GiB | 6.06 GiB | 7.37 GiB | Recommended 4-bit |
| MLX_4BIT | 4.50 | 4.06 GiB | 5.74 GiB | 7.05 GiB | Apple Silicon Optimized |
| IQ4_XS | 4.25 | 3.84 GiB | 5.52 GiB | 6.83 GiB | Compact 4-bit |
| Q3_K_M | 3.91 | 3.53 GiB | 5.21 GiB | 6.52 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 | 7.4 / 140.4 GiB | 612.9 tok/s | 0.13s | 32k | Simulate → |
| Cloud NVIDIA H100 SXM (80 GB) | COMFORTABLE | 7.4 / 79.4 GiB | 427.7 tok/s | 0.13s | 32k | Simulate → |
| Cloud NVIDIA A100 SXM (80 GB) | COMFORTABLE | 7.4 / 79.4 GiB | 254.2 tok/s | 0.34s | 32k | Simulate → |
| NVIDIA RTX PRO 6000 Blackwell (96 GB) | COMFORTABLE | 7.4 / 95.0 GiB | 228.8 tok/s | 0.42s | 32k | Simulate → |
| NVIDIA RTX 5090 (32 GB) | COMFORTABLE | 7.4 / 31.0 GiB | 223.4 tok/s | 0.52s | 32k | Simulate → |
| NVIDIA RTX 4090 (24 GB) | COMFORTABLE | 7.4 / 23.0 GiB | 125.6 tok/s | 0.65s | 32k | Simulate → |
| NVIDIA RTX 5080 (16 GB) | COMFORTABLE | 7.4 / 15.0 GiB | 119.7 tok/s | 0.95s | 32k | Simulate → |
| NVIDIA RTX 3090 (24 GB Used) | COMFORTABLE | 7.4 / 23.0 GiB | 113.8 tok/s | 1.53s | 32k | Simulate → |
| NVIDIA RTX 5070 Ti (16 GB) | COMFORTABLE | 7.4 / 15.0 GiB | 111.7 tok/s | 1.20s | 32k | Simulate → |
| AMD Radeon RX 7900 XTX (24 GB) | COMFORTABLE | 7.4 / 23.0 GiB | 110.9 tok/s | 1.06s | 32k | Simulate → |
| Cloud NVIDIA L40S (48 GB) | COMFORTABLE | 7.4 / 47.4 GiB | 107.7 tok/s | 0.31s | 32k | Simulate → |
| Apple Mac Studio M3 Ultra (256 GB) | COMFORTABLE | 7.2 / 199.7 GiB | 97.1 tok/s | 1.99s | 32k | Simulate → |
| Apple Mac Studio M3 Ultra (512 GB) | COMFORTABLE | 7.2 / 399.4 GiB | 97.1 tok/s | 1.99s | 32k | Simulate → |
| 2x NVIDIA RTX 3090 Rig (48 GB) | COMFORTABLE | 7.4 / 46.6 GiB | 95.6 tok/s | 1.87s | 32k | Simulate → |
| Apple Mac Studio M2 Ultra (192 GB) | COMFORTABLE | 7.2 / 149.8 GiB | 94.8 tok/s | 2.19s | 32k | Simulate → |
| Apple Mac Studio M4 Max (64 GB) | COMFORTABLE | 7.2 / 49.9 GiB | 63.9 tok/s | 3.01s | 32k | Simulate → |
| Apple Mac Studio M4 Max (128 GB) | COMFORTABLE | 7.2 / 99.8 GiB | 63.9 tok/s | 3.01s | 32k | Simulate → |
| NVIDIA RTX 5060 Ti (16 GB) | COMFORTABLE | 7.4 / 15.0 GiB | 54.5 tok/s | 2.27s | 32k | Simulate → |
| Apple MacBook Pro M3 Max (64 GB) | COMFORTABLE | 7.2 / 49.9 GiB | 46.2 tok/s | 4.02s | 32k | Simulate → |
| Apple MacBook Pro M3 Max (128 GB) | COMFORTABLE | 7.2 / 99.8 GiB | 46.2 tok/s | 4.02s | 32k | Simulate → |
| NVIDIA RTX 3060 (12 GB) | COMFORTABLE | 7.4 / 11.0 GiB | 42.7 tok/s | 4.32s | 32k | Simulate → |
| Apple Mac Mini M4 Pro (48 GB) | COMFORTABLE | 7.2 / 37.4 GiB | 30.7 tok/s | 6.18s | 32k | Simulate → |
| Apple Mac Mini M4 Pro (64 GB) | COMFORTABLE | 7.2 / 49.9 GiB | 30.7 tok/s | 6.18s | 32k | Simulate → |
| Ryzen AI Max+ 395 Strix Halo (128 GB) | COMFORTABLE | 7.2 / 96.0 GiB | 28.8 tok/s | 2.25s | 32k | Simulate → |
| Ryzen AI Max+ 395 Strix Halo (64 GB) | COMFORTABLE | 7.2 / 48.0 GiB | 28.8 tok/s | 2.25s | 32k | Simulate → |
| Apple Mac Mini M4 (16 GB) | COMFORTABLE | 7.2 / 10.0 GiB | 13.1 tok/s | 13.66s | 32k | Simulate → |
| Apple Mac Mini M4 (24 GB) | COMFORTABLE | 7.2 / 18.0 GiB | 13.1 tok/s | 13.66s | 32k | Simulate → |
| Ryzen AI 9 HX 370 Mini-PC (64 GB) | COMFORTABLE | 7.2 / 48.0 GiB | 12.8 tok/s | 6.38s | 32k | Simulate → |
Interactive Qwen2.5 7B Instruct Simulator
7.6B 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: 32k
Memory Allocation Footprint 7.37 / 95.00 GiB usable
Weights: 4.38 GiB
KV Cache: 1.75 GiB
Overhead: 1.24 GiB
Headroom: 87.63 GiB
Decode Speed
228.8
tok/s (batch=1)
Prefill Speed
10610
prompt tok/s
TTFT Latency
0.42s
for 4,096 tok
Active Params
7.6B
of 7.6B total
llama.cpp Server Command
llama-server -hf Qwen/Qwen2.5-7B-Instruct:Q4_K_M -ngl 99 -fa on -c 32768 --port 8080| Context | KV Cache | Total VRAM | Decode | Status |
|---|---|---|---|---|
| 4k | 0.22 GiB | 5.59 GiB | 305.1 tok/s | COMFORTABLE |
| 8k | 0.44 GiB | 6.06 GiB | 291.2 tok/s | COMFORTABLE |
| 16k | 0.88 GiB | 6.49 GiB | 266.9 tok/s | COMFORTABLE |
| 32k | 1.75 GiB | 7.37 GiB | 228.8 tok/s | COMFORTABLE |
Buy-vs-Cloud-vs-API Monthly TCO Break-Even
USE SERVERLESS APIOwn Hardware/mo
$161.32
19.5h active + power
Cloud GPU/mo
$41.53
@$1.85/hr spot/pod
Serverless API/mo
$3.80
API cheaper
Recommended Hardware & Cloud Deployment
Contextual to fit statusEngineering FAQ — Qwen2.5 7B Instruct
How much VRAM does Qwen2.5 7B Instruct require in Q4_K_M?
Qwen2.5 7B Instruct weights occupy 4.38 GiB in Q4_K_M. Including 32k FP16 KV cache and runtime buffers, total memory is 7.37 GiB.
What is the cheapest local hardware that runs Qwen2.5 7B Instruct?
NVIDIA RTX PRO 6000 Blackwell (96 GB) (96 GB) runs Qwen2.5 7B Instruct Q4_K_M at ~228.8 tok/s decode and 0.42s TTFT.
How does Qwen2.5 7B Instruct scale KV cache at long context?
Qwen2.5 7B Instruct uses GQA with 4 KV heads across 28 layers (128 head_dim).