Inference Memory & Cost Modeling
Local LLM VRAM & inference speed calculator
Calculate whether open-weights models fit your GPU VRAM or Apple Mac unified memory, estimate generation speeds (tokens/sec) from memory bandwidth, and compute break-even points versus frontier API costs.
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 |
Local Hardware Configurations & Pricing
High-bandwidth VRAM specs and current market pricesConsumer 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 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
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
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
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Pre-Calculated Benchmark Directory
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