HARDWARE BENCHMARK & VRAM ENVELOPE โข CONSUMER GPU16 GB VRAM โข 960 GB/s
What Models Can Run on NVIDIA GeForce RTX 5080 (16GB GDDR7)?
16 GB VRAM960 GB/s Bandwidth360W TDPMSRP: $999Cloud: $0.45/hr on Vast.ai
NVIDIA GeForce RTX 5080 (16GB GDDR7) provides 16 GB of usable memory with 960 GB/s memory bandwidth at 360W TDP. Blazing fast 8Bโ14B coding models with FP4/Q6_K precision.
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 |
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All Open-Weights Models Tested on NVIDIA GeForce RTX 5080 (16GB GDDR7) (Q4_K_M @ 32K Context)
Standard Q4_K_M precision with 32K context window and Q8_0 KV cache
| Model | Architecture | Total VRAM Needed | Headroom | Est. Decode Speed | Compatibility | Detailed Config |
|---|---|---|---|---|---|---|
| Qwen 3.8 27B Instruct Alibaba Cloud | 27.2B Dense | 21.93 GB(137%) | -5.93 GB | 0 (OOM) | โ OOM | Configure CLI โ |
| Qwen 3.6 35B-A3B MoE Alibaba Cloud | 35B MoE | 24.1 GB(151%) | -8.1 GB | 0 (OOM) | โ OOM | Configure CLI โ |
| DeepSeek-V4.1-Flash (552B Engram MoE) DeepSeek | 552B MoE | 345.59 GB(2160%) | -329.59 GB | 0 (OOM) | โ OOM | Configure CLI โ |
| Meta Muse Spark Open (816B-A42B MoE) Meta AI | 816B MoE | 512.86 GB(3205%) | -496.86 GB | 0 (OOM) | โ OOM | Configure CLI โ |
| Google Gemma 4 26B MoE Google | 26.4B MoE | 20.23 GB(126%) | -4.23 GB | ~44 tok/s | โก PARTIAL | Configure CLI โ |
| Google Gemma 4 E4B (Edge 4B) Google | 4.1B Dense | 4.24 GB(27%) | +11.76 GB | ~242 tok/s | โ FITS | Configure CLI โ |
| DeepSeek-R1 Distill Qwen 32B DeepSeek | 32.8B Dense | 25.41 GB(159%) | -9.41 GB | 0 (OOM) | โ OOM | Configure CLI โ |
| DeepSeek-R1 Distill Qwen 14B DeepSeek | 14.8B Dense | 13.16 GB(82%) | +2.84 GB | ~67 tok/s | โ FITS | Configure CLI โ |
| Meta Llama 4 Scout (109B-A17B MoE) Meta AI | 109B MoE | 71.66 GB(448%) | -55.66 GB | 0 (OOM) | โ OOM | Configure CLI โ |
| Meta Llama 3.3 70B Instruct Meta AI | 70.6B Dense | 49.96 GB(312%) | -33.96 GB | 0 (OOM) | โ OOM | Configure CLI โ |
| Meta Llama 3.1 8B Instruct Meta AI | 8B Dense | 7.87 GB(49%) | +8.13 GB | ~122 tok/s | โ FITS | Configure CLI โ |
| Microsoft Phi-4 Mini 3.8B Microsoft | 3.8B Dense | 3.92 GB(25%) | +12.08 GB | ~263 tok/s | โ FITS | Configure CLI โ |