VRAM TIER DIRECTORY โข 48 GB ENVELOPE8 of 12 Models Fit
Best Local LLMs for 48GB VRAM
Typical Hardware:2x NVIDIA RTX 3090 (24GB x 2)2x RTX 4090 48GBNVIDIA RTX 6000 Ada 48GB
The ultimate price-to-performance homelab rig (~$1,550 buy). Easily fits 70B dense models (Llama 3.3 70B) at Q4_K_M with 32K context and 27B models at full uncompressed precision.
โ ๏ธ Exceeds VRAM โ Requires Partial CPU/RAM Offload (Slow)49.96 GB (104%)~3 t/s
Meta Llama 3.3 70B Instruct on 2x NVIDIA RTX 3090 Dual Rig (48GB Total VRAM)
โ ๏ธ Partial Offload โ Slow CPU/RAM Spillover
โก PARTIAL OFFLOAD
Required VRAM
49.96 GB
104% of hardware capacity (48 GB)Est. Decode Speed
~3 tok/s
Dense model @ 936 GB/sAvailable Headroom
-1.96 GB
Needs +1.96 GB more VRAMMemory Allocation & Headroom
49.96 GB allocated of 48 GB capacity
104% utilized
Weights: 42.36 GB
KV: (32K): 5.37 GB
Overhead: 2.23 GB
Deficit: -1.96 GB
โก Architecture & Throughput Transparency: Single-user decode speed (~3 tok/s) is memory-bandwidth bound at batch size = 1 on 2x NVIDIA RTX 3090 Dual Rig (48GB Total VRAM) (936 GB/s). Prompt prefill / TTFT (~44 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 llama3.3:70bโ ๏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. (Meta Llama 3.3 70B Instruct @ 49.96 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 | 141.2 GB | 5.37 GB | 152.26 GB (317%) | 0 t/s | OOM |
| Q8_08.5 bpw | 74.84 GB | 5.37 GB | 83.58 GB (174%) | 0 t/s | OOM |
| Q6_K6.56 bpw | 57.89 GB | 5.37 GB | 66.04 GB (138%) | 0 t/s | OOM |
| Q5_K_M5.68 bpw | 50.13 GB | 5.37 GB | 58 GB (121%) | ~2 t/s | PARTIAL |
| Q4_K_M4.8 bpw | 42.36 GB | 5.37 GB | 49.96 GB (104%) | ~3 t/s | PARTIAL |
| FP44.25 bpw | 37.42 GB | 5.37 GB | 44.85 GB (93%) | ~15 t/s | FITS |
| IQ3_XS3.3 bpw | 28.95 GB | 5.37 GB | 36.08 GB (75%) | ~19 t/s | FITS |
Local Hardware Configurations & Pricing
High-bandwidth VRAM specs and current market pricesMulti-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
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
Consumer 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
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 48GB 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 |
|---|---|---|---|---|---|---|
| Qwen 3.6 35B-A3B MoE Ultra-Fast 100+ tok/s Coding & Local RAG on 24GB GPUs | 35B MoE | 24.1 GB | +23.9 GB free | ~263 t/s | FITS | ollama run qwen3.6:35b-a3b |
| Microsoft Phi-4 Mini 3.8B Ultra-Light Laptop & CPU/Integrated GPU Inference | 3.8B Dense | 3.92 GB | +44.1 GB free | ~234 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 | +43.8 GB free | ~215 t/s | FITS | ollama run gemma4:4b |
| Google Gemma 4 26B MoE High-Speed Consumer GPU & Apple Silicon Daily Assistant | 26.4B MoE | 20.23 GB | +27.8 GB free | ~176 t/s | FITS | ollama run gemma4:26b |
| Meta Llama 3.1 8B Instruct Fast 8GBโ12GB GPU Chat, Classification & Local Agents | 8B Dense | 7.87 GB | +40.1 GB free | ~109 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 | +34.8 GB free | ~60 t/s | FITS | ollama run deepseek-r1:14b |
| Qwen 3.8 27B Instruct Agentic Coding, Terminal Workflows & Multi-File Refactoring | 27.2B Dense | 21.93 GB | +26.1 GB free | ~34 t/s | FITS | ollama run qwen3.8:27b |
| DeepSeek-R1 Distill Qwen 32B Local Step-by-Step Math, Logic & Code Verification | 32.8B Dense | 25.41 GB | +22.6 GB free | ~28 t/s | FITS | ollama run deepseek-r1:32b |
| Meta Llama 3.3 70B Instruct Standard 70B Dense Enterprise Baseline for 48GBโ64GB Rigs | 70.6B Dense | 49.96 GB | -2 GB short | ~3 t/s | PARTIAL | 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 | -297.6 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 | -464.9 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 | -23.7 GB short | 0 (OOM) | OOM | Deploy on Cloud GPU โ |
Need to Run Models Larger than 48GB?
If your preferred model exceeds your local 48GB 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.