MODEL ARCHITECTURE SPEC โข ALIBABA CLOUD27.2B Dense
Qwen 3.8 27B Instruct VRAM & inference speed
27.2B Total ParamsDense Architecture64 Layers โข 8 KV Heads32K Default Ctx (Max 128K)Ollama: qwen3.8:27b
Accurate memory requirements, KV cache footprint, and tokens/sec generation benchmarks for running Qwen 3.8 27B Instruct across local GPUs, Apple Silicon Macs, and cloud instances.
โก Fits in VRAM (Tight Headroom โ Use Q8/Q4 KV Cache)21.93 GB (91%)~40 t/s
Qwen 3.8 27B Instruct on NVIDIA GeForce RTX 4090 (24GB GDDR6X)
โก Tight Fit โ Runs 100% on GPU (Low Headroom)
โก TIGHT FIT
Required VRAM
21.93 GB
91% of hardware capacity (24 GB)Est. Decode Speed
~40 tok/s
Dense model @ 1008 GB/sAvailable Headroom
+2.07 GB
Safe for OS display & full contextMemory Allocation & Headroom
21.93 GB allocated of 24 GB capacity
91% utilized
Weights: 16.32 GB
KV: (32K): 4.29 GB
Overhead: 1.32 GB
Free Headroom: +2.07 GB
โก Architecture & Throughput Transparency: Single-user decode speed (~40 tok/s) is memory-bandwidth bound at batch size = 1 on NVIDIA GeForce RTX 4090 (24GB GDDR6X) (1008 GB/s). Prompt prefill / TTFT (~580 tok/s) is compute-bound and utilizes GPU Tensor cores. Actual latency varies with FlashAttention-2 and runtime engine (Ollama/vLLM/llama.cpp).
OLLAMA_FLASH_ATTENTION=1 OLLAMA_KV_CACHE_TYPE=q8_0 ollama run qwen3.8:27bCloud Instance Options for Qwen 3.8 27B Instruct
On-DemandDeploy this model on an on-demand cloud GPU without local hardware constraints or power usage:
RunPod Cloud GPUsBest 1-Click Ollama & vLLM
$0.34/hr (RTX 4070 Ti) โ $2.39/hr (H100)Vast.ai GPU MarketplaceCheapest Dual RTX 3090 / 4090
$0.18/hr (RTX 3060) โ $0.55/hr (RTX 4090)Lambda GPU CloudEnterprise 8x H100 Clusters
$1.99/hr (GH200 / H100)Quantization Precision Matrix
32K ctx โข KV: Q8_0| Quantization | Weights | KV Cache | Total VRAM | Est. Speed | Fit Status |
|---|---|---|---|---|---|
| FP1616 bpw | 54.4 GB | 4.29 GB | 61.34 GB (256%) | 0 t/s | OOM |
| Q8_08.5 bpw | 28.83 GB | 4.29 GB | 34.88 GB (145%) | 0 t/s | OOM |
| Q6_K6.56 bpw | 22.3 GB | 4.29 GB | 28.12 GB (117%) | ~6 t/s | PARTIAL |
| Q5_K_M5.68 bpw | 19.31 GB | 4.29 GB | 25.03 GB (104%) | ~7 t/s | PARTIAL |
| Q4_K_M4.8 bpw | 16.32 GB | 4.29 GB | 21.93 GB (91%) | ~40 t/s | FITS |
| FP44.25 bpw | 14.42 GB | 4.29 GB | 19.96 GB (83%) | ~44 t/s | FITS |
| IQ3_XS3.3 bpw | 11.15 GB | 4.29 GB | 16.58 GB (69%) | ~56 t/s | FITS |
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Hardware Compatibility Matrix for Qwen 3.8 27B Instruct
Standard Q4_K_M precision with 32K context window and Q8_0 KV cache
| Hardware System | VRAM Envelope | Required VRAM | Headroom | Est. Decode Speed | Compatibility | Test Fit |
|---|---|---|---|---|---|---|
| NVIDIA GeForce RTX 5090 (32GB GDDR7) Consumer GPU โข 1792 GB/s | 32 GB | 21.93 GB | +10.07 GB | ~70 tok/s | โ FITS | Test Fit โ |
| NVIDIA GeForce RTX 4090 (24GB GDDR6X) Consumer GPU โข 1008 GB/s | 24 GB | 21.93 GB | +2.07 GB | ~40 tok/s | โก TIGHT FIT | Test Fit โ |
| 2x NVIDIA RTX 3090 Dual Rig (48GB Total VRAM) Multi-GPU Rig โข 936 GB/s | 48 GB | 21.93 GB | +26.07 GB | ~34 tok/s | โ FITS | Test Fit โ |
| NVIDIA GeForce RTX 5080 (16GB GDDR7) Consumer GPU โข 960 GB/s | 16 GB | 21.93 GB | -5.93 GB | 0 (OOM) | โ OOM | Test Fit โ |
| NVIDIA RTX 4070 Ti Super (16GB) Consumer GPU โข 672 GB/s | 16 GB | 21.93 GB | -5.93 GB | 0 (OOM) | โ OOM | Test Fit โ |
| NVIDIA GeForce RTX 3060 (12GB) Consumer GPU โข 360 GB/s | 12 GB | 21.93 GB | -9.93 GB | 0 (OOM) | โ OOM | Test Fit โ |
| Apple Mac Mini M4 Pro (64GB Unified Memory) Apple Silicon โข 273 GB/s | 54 GB | 21.93 GB | +32.07 GB | ~10 tok/s | โ FITS | Test Fit โ |
| Apple Mac Studio M4 Max (128GB Unified Memory) Apple Silicon โข 546 GB/s | 112 GB | 21.93 GB | +90.07 GB | ~20 tok/s | โ FITS | Test Fit โ |
| Apple Mac Studio M5 Ultra (256GB Unified Memory) Apple Silicon โข 890 GB/s | 236 GB | 21.93 GB | +214.07 GB | ~33 tok/s | โ FITS | Test Fit โ |
| AMD Strix Halo Mini-PC (Ryzen AI Max+ 395 128GB) Unified APU / Mini-PC โข 256 GB/s | 110 GB | 21.93 GB | +88.07 GB | ~8 tok/s | โ FITS | Test Fit โ |
| NVIDIA DGX Spark (128GB Grace-Blackwell Unified) AI Appliance โข 273 GB/s | 118 GB | 21.93 GB | +96.07 GB | ~11 tok/s | โ FITS | Test Fit โ |
| NVIDIA H100 SXM (80GB HBM3 Cloud GPU) Datacenter Cloud GPU โข 3350 GB/s | 80 GB | 21.93 GB | +58.07 GB | ~139 tok/s | โ FITS | Test Fit โ |