HARDWARE FIT CHECK โข Qwen 3.8 27B Instruct + NVIDIA GeForce RTX 5080 (16GB GDDR7)21.93 GB / 16 GB VRAM
Can I Run Qwen 3.8 27B Instruct on NVIDIA GeForce RTX 5080 (16GB GDDR7)?
Full VRAM footprint analysis, tokens/sec generation benchmark, and copy-paste Ollama & vLLM runtime commands for running Qwen 3.8 27B Instruct (27.2B) on NVIDIA GeForce RTX 5080 (16GB GDDR7) (16 GB).
โ Out of Memory (OOM) โ Exceeds Hardware VRAM21.93 GB (137%)0 t/s
Qwen 3.8 27B Instruct on NVIDIA GeForce RTX 5080 (16GB GDDR7)
โ Out of Memory โ Exceeds VRAM Capacity
โ OOM
Required VRAM
21.93 GB
137% of hardware capacity (16 GB)Est. Decode Speed
0 tok/s
Dense model @ 960 GB/sAvailable Headroom
-5.93 GB
Needs +5.93 GB more VRAMMemory Allocation & Headroom
21.93 GB allocated of 16 GB capacity
137% utilized
Weights: 16.32 GB
KV: (32K): 4.29 GB
Overhead: 1.32 GB
Deficit: -5.93 GB
โก Architecture & Throughput Transparency: Single-user decode speed (~0 tok/s) is memory-bandwidth bound at batch size = 1 on NVIDIA GeForce RTX 5080 (16GB GDDR7) (960 GB/s). Prompt prefill / TTFT (~0 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 qwen3.8:27bโ ๏ธ Out of VRAM? Don't downgrade model quality.
Out of VRAMSpin up an on-demand cloud GPU instance in 60 seconds with pre-installed Ollama and vLLM. (Qwen 3.8 27B Instruct @ 21.93 GB VRAM)
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 (383%) | 0 t/s | OOM |
| Q8_08.5 bpw | 28.83 GB | 4.29 GB | 34.88 GB (218%) | 0 t/s | OOM |
| Q6_K6.56 bpw | 22.3 GB | 4.29 GB | 28.12 GB (176%) | 0 t/s | OOM |
| Q5_K_M5.68 bpw | 19.31 GB | 4.29 GB | 25.03 GB (156%) | 0 t/s | OOM |
| Q4_K_M4.8 bpw | 16.32 GB | 4.29 GB | 21.93 GB (137%) | 0 t/s | OOM |
| FP44.25 bpw | 14.42 GB | 4.29 GB | 19.96 GB (125%) | ~9 t/s | PARTIAL |
| IQ3_XS3.3 bpw | 11.15 GB | 4.29 GB | 16.58 GB (104%) | ~12 t/s | PARTIAL |
Local Hardware Configurations & Pricing
High-bandwidth VRAM specs and current market pricesConsumer GPU16 GB VRAM
NVIDIA GeForce RTX 5080 (16GB GDDR7)
Blazing fast 8Bโ14B coding models with FP4/Q6_K precision
960 GB/s360W TDP
$999Amazon Prime โ
Consumer GPU32 GB VRAM
NVIDIA GeForce RTX 5090 (32GB GDDR7)
Maximum tokens/sec on 27Bโ35B models & Blackwell FP4 quantization
1792 GB/s575W TDP
$1,999Amazon Prime โ
Consumer GPU24 GB VRAM
NVIDIA GeForce RTX 4090 (24GB GDDR6X)
The 24GB CUDA enthusiast standard for 14Bโ32B Q4_K_M models
1008 GB/s450W TDP
$1,699Amazon Prime โ
Multi-GPU Rig48 GB VRAM
2x NVIDIA RTX 3090 Dual Rig (48GB Total VRAM)
Best price-to-VRAM homelab rig for 70B Q4_K_M & Qwen 3.8 27B Q8
936 GB/s700W TDP
$1,550Amazon Prime โ
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