VRAM TIER DIRECTORY โข 16 GB ENVELOPE4 of 12 Models Fit
Best Local LLMs for 16GB VRAM
Typical Hardware:NVIDIA GeForce RTX 5080 16GBRTX 4070 Ti Super 16GBRTX 4080 16GB
16GB VRAM is the sweet spot for 14B Q8_0 near-lossless models and 26Bโ35B MoE models (Qwen 3.6 35B-A3B, Gemma 4 26B MoE) at Q4_K_M, delivering 60โ100+ tokens/sec.
โ Out of Memory (OOM) โ Exceeds Hardware VRAM24.1 GB (151%)0 t/s
Qwen 3.6 35B-A3B MoE on NVIDIA GeForce RTX 5080 (16GB GDDR7)
โ Out of Memory โ Exceeds VRAM Capacity
โ OOM
Required VRAM
24.1 GB
151% of hardware capacity (16 GB)Est. Decode Speed
0 tok/s
3B active params @ 960 GB/sAvailable Headroom
-8.1 GB
Needs +8.1 GB more VRAMMemory Allocation & Headroom
24.1 GB allocated of 16 GB capacity
151% utilized
Weights: 21 GB
KV: (32K): 1.61 GB
Overhead: 1.49 GB
Deficit: -8.1 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 qwen3.6:35b-a3bโ ๏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 3.6 35B-A3B MoE @ 24.1 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 | 70 GB | 1.61 GB | 74.81 GB (468%) | 0 t/s | OOM |
| Q8_08.5 bpw | 37.1 GB | 1.61 GB | 40.76 GB (255%) | 0 t/s | OOM |
| Q6_K6.56 bpw | 28.7 GB | 1.61 GB | 32.06 GB (200%) | 0 t/s | OOM |
| Q5_K_M5.68 bpw | 24.85 GB | 1.61 GB | 28.08 GB (176%) | 0 t/s | OOM |
| Q4_K_M4.8 bpw | 21 GB | 1.61 GB | 24.1 GB (151%) | 0 t/s | OOM |
| FP44.25 bpw | 18.55 GB | 1.61 GB | 21.56 GB (135%) | ~72 t/s | PARTIAL |
| IQ3_XS3.3 bpw | 14.35 GB | 1.61 GB | 17.21 GB (108%) | ~88 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
Export Full Architecture & Infrastructure Spec
Get an executive-ready PDF report with exact memory math, hardware benchmarks, and cost comparisons.
All Open Models Evaluated on 16GB VRAM
Standard Q4_K_M quantization with 32K context window and Q8_0 KV cache
| Model | Architecture | VRAM Required | Headroom | Est. Decode Speed | Compatibility | Ollama Command |
|---|---|---|---|---|---|---|
| Microsoft Phi-4 Mini 3.8B Ultra-Light Laptop & CPU/Integrated GPU Inference | 3.8B Dense | 3.92 GB | +12.1 GB free | ~263 t/s | FITS | ollama run phi4-mini:3.8b |
| Google Gemma 4 E4B (Edge 4B) 8GB Laptop GPUs, Local Autocomplete & Fast Summarization | 4.1B Dense | 4.24 GB | +11.8 GB free | ~242 t/s | FITS | ollama run gemma4:4b |
| Meta Llama 3.1 8B Instruct Fast 8GBโ12GB GPU Chat, Classification & Local Agents | 8B Dense | 7.87 GB | +8.1 GB free | ~122 t/s | FITS | ollama run llama3.1:8b |
| DeepSeek-R1 Distill Qwen 14B Mainstream 12GBโ16GB Gaming GPUs Reasoning Assistant | 14.8B Dense | 13.16 GB | +2.8 GB free | ~67 t/s | FITS | ollama run deepseek-r1:14b |
| Google Gemma 4 26B MoE High-Speed Consumer GPU & Apple Silicon Daily Assistant | 26.4B MoE | 20.23 GB | -4.2 GB short | ~44 t/s | PARTIAL | Deploy on Cloud GPU โ |
| Qwen 3.8 27B Instruct Agentic Coding, Terminal Workflows & Multi-File Refactoring | 27.2B Dense | 21.93 GB | -5.9 GB short | 0 (OOM) | OOM | Deploy on Cloud GPU โ |
| Qwen 3.6 35B-A3B MoE Ultra-Fast 100+ tok/s Coding & Local RAG on 24GB GPUs | 35B MoE | 24.1 GB | -8.1 GB short | 0 (OOM) | OOM | Deploy on Cloud GPU โ |
| DeepSeek-V4.1-Flash (552B Engram MoE) Frontier SWE Coding (77.0 DeepSWE), 1M Context & 3D/Game Synthesis | 552B MoE | 345.59 GB | -329.6 GB short | 0 (OOM) | OOM | Deploy on Cloud GPU โ |
| Meta Muse Spark Open (816B-A42B MoE) Frontier Multi-Step Agentic Reasoning & Native Multimodal | 816B MoE | 512.86 GB | -496.9 GB short | 0 (OOM) | OOM | Deploy on Cloud GPU โ |
| DeepSeek-R1 Distill Qwen 32B Local Step-by-Step Math, Logic & Code Verification | 32.8B Dense | 25.41 GB | -9.4 GB short | 0 (OOM) | OOM | Deploy on Cloud GPU โ |
| Meta Llama 4 Scout (109B-A17B MoE) Long-Context Codebase Indexing & Multi-Document Analysis | 109B MoE | 71.66 GB | -55.7 GB short | 0 (OOM) | OOM | Deploy on Cloud GPU โ |
| Meta Llama 3.3 70B Instruct Standard 70B Dense Enterprise Baseline for 48GBโ64GB Rigs | 70.6B Dense | 49.96 GB | -34 GB short | 0 (OOM) | OOM | Deploy on Cloud GPU โ |
Need to Run Models Larger than 16GB?
If your preferred model exceeds your local 16GB GPU, you can rent high-memory on-demand cloud GPUs with zero upfront hardware costs:
24GBโ32GB Cloud VRAM
Deploy on RunPod ($0.59/hr) โRent RTX 4090 / RTX 5090
Boot an on-demand RTX 4090 (24GB) from $0.59/hr or RTX 5090 (32GB) from $0.89/hr on RunPod.
48GBโ80GB High VRAM
Rent on Vast.ai ($0.78/hr) โRent Dual 3090 or H100
Dual 3090 (48GB) from $0.78/hr on Vast.ai, or H100 (80GB HBM3) from $2.39/hr for 70B+ models.