LLM VRAM Calculator (2026): Local GPU Fit, GGUF Quantization, KV Cache & Tokens/Sec
Calculate exact GPU VRAM requirements (Model Weights + Context KV Cache + Runtime Buffers) for Llama 4, DeepSeek-R1, Qwen 3, Gemma 3, and custom LLMs across FP16, Q8_0, Q6_K, Q4_K_M, IQ3_XS, and 1.58-bit GGUF. Test whether your model fits on an NVIDIA RTX 5090 (32GB), RTX 4090 (24GB), Dual RTX 3090 (48GB), or Apple Mac Studio—and estimate real-world tokens per second (tok/s).
Built by R.K. · Last verified: · Checked against NVIDIA GeForce RTX 5090 Official Specs (32GB GDDR7 · 1,792 GB/s), RunPod RTX 5090 AI Benchmarks, and GGUF / llama.cpp KV Cache Formulas.
🧮 Interactive LLM VRAM, GPU Fit & Tokens/Sec Simulator
Step 1: Model Architecture, Quantization & Context Window
Dictates static model weight size in VRAM.
Equals Total Params for Dense models; smaller for MoE (dictates tok/s!).
Use
-ctk q8_0 -ctv q8_0 in llama.cpp to halve KV cache VRAM.Your LLM VRAM & Hardware Fit Verdict
Total VRAM Required
24.38 GB
✅ Fits in 32 GB (7.62 GB headroom left)
Est. Generation Speed (Tok/s)
58.6 tok/s
On NVIDIA RTX 5090 (1,792 GB/s)
Model Weights vs. KV Cache
19.70 GB
+ 2.45 GB KV Cache + 2.23 GB Buffer
Max Context Before OOM
134K tokens
On 32 GB with Q8_0 KV cache
1. Static Model Weights (32.5B × 4.85 bpw ÷ 8)19.70 GB
2. Context Window KV Cache (32,768 tokens)2.45 GB
3. CUDA / Metal / Compute Graph Overhead2.23 GB
Recommended Minimum Hardware TierRTX 5090 (32GB) or Dual 3090 (48GB)
🧾 Shareable Local LLM Rig Receipt
24.4 GB VRAM · 58.6 tok/s
My 2026 Local LLM Build: 32.5B @ Q4_K_M (32K ctx, Q8 KV) = 24.38 GB VRAM (19.70 GB weights + 2.45 GB KV + 2.23 GB buffer) → Fits 100% on NVIDIA RTX 5090 (32 GB) @ ~58.6 tok/s! Sourced via UltimateInfoGuide.com/llm-vram-calculator/
Live Hardware Fit & Tokens/Sec Comparison for Your Selected Model (32.5B @ Q4_K_M · 24.38 GB Required)
| GPU / Apple Silicon Hardware | VRAM / Unified Cap | Memory Bandwidth | Fit Status for Your Model | Est. Generation Speed (Tok/s) | Est. Street / Cloud Price |
|---|---|---|---|---|---|
| NVIDIA RTX 5080 / 4080 Super | 16 GB GDDR7/6X | 960 GB/s | ❌ Needs Offload (-8.4 GB) | ~8.5 tok/s (PCIe bottleneck) | $999 – $1,199 |
| NVIDIA RTX 4090 / Used 3090 | 24 GB GDDR6X | 1,008 GB/s | ⚠️ Tight / Partial Offload | ~28.4 tok/s | $750 (3090) – $1,799 |
| NVIDIA GeForce RTX 5090 | 32 GB GDDR7 | 1,792 GB/s | ✅ 100% GPU Fit (+7.6 GB free) | ~58.6 tok/s | $1,999 – $2,299 |
| Dual 2× NVIDIA RTX 3090 Rig | 48 GB GDDR6X | 936 GB/s | ✅ 100% GPU Fit (+23.6 GB free) | ~29.1 tok/s | ~$1,500 (Used GPUs) |
| Dual 2× NVIDIA RTX 5090 Rig | 64 GB GDDR7 | 1,792 GB/s | ✅ 100% GPU Fit (+39.6 GB free) | ~55.4 tok/s | ~$4,200 (2× GPUs) |
| Mac Studio M4 Max (128 GB) | 128 GB Unified (~96 GB GPU) | 546 GB/s | ✅ 100% Unified Fit | ~17.8 tok/s | $3,499 |
| Mac Studio M3/M4 Ultra (192 GB) | 192 GB Unified (~150 GB GPU) | 819 GB/s | ✅ 100% Unified Fit | ~26.8 tok/s | $5,499 |
| NVIDIA H100 SXM Cloud Node | 80 GB HBM3 | 3,350 GB/s | ✅ 100% HBM3 Fit | ~109.5 tok/s | ~$2.49 / hr Cloud |
Step 1: Local Rig Cost, Power Draw & Cloud Hourly Rate
E.g. $2,800 for an RTX 5090 build or $3,499 for Mac Studio 128GB.
32GB+ GPUs and high-RAM Mac Studios typically retain 50%–65% value.
RTX 5090 rig ≈ 520W · Dual 3090 ≈ 720W · Mac Studio ≈ 95W.
U.S. average is ~$0.17/kWh (CA/Northeast/EU ~$0.32/kWh).
Local GPU Rig vs. Cloud Rental Break-Even Verdict
Net Break-Even Cloud Hours
2,096 hrs
Breaks even in 15.3 months at 4.5 hrs/day
2-Year Net Local Rig Cost
$1,551
$1,260 depreciation + $291 electricity
2-Year Cloud Rental Cost
$2,267
3,285 hours × $0.69/hr on RunPod
Local Electricity Cost / Hour
$0.088 / hr
$12.08/mo power bill at 4.5 hrs/day
Net Hardware Cost After 2-Year Resale$1,260 ($2,800 − $1,540 resale)
Net Hourly Savings vs. Cloud (After Electricity)+$0.602 / hr saved
2-Year Total Hours Modeled3,285 hours over 24 mos
✅ 3 Local LLM Memory Rules Every Builder Should Know in 2026:
(1) The KV Cache Context Trap: A 32B model at Q4_K_M takes only 19.7 GB for static weights, but expanding the context window to 64K–128K in FP16 can add 8 to 16 GB of KV cache—pushing a 32GB RTX 5090 into out-of-memory (OOM). Turning on Q8_0 KV cache quantization cuts context memory by 50% with near-zero perplexity loss! (2) MoE Capacity vs. Speed: Mixture-of-Experts models like Llama 4 Scout (109B total / 17B active) require enough VRAM to hold all 109B parameters (~66 GB at Q4_K_M), but generate tokens at the blistering speed of a 17B model! (3) Never Spill Weights Across PCIe RAM: Offloading even 15% of layers to system DDR5 RAM drops RTX 5090 generation speed from ~60 tok/s down to ~6–9 tok/s.
Quick Answer: How do you calculate VRAM needed for a local LLM?
Use the formula Total VRAM (GB) = [(Parameters in Billions × Bits per Weight) ÷ 8] + KV Cache + 1.5–2.5 GB Overhead. For standard Q4_K_M (4.85 bits/weight) GGUF quantization with a 16K–32K context window, multiply the model’s parameter count in billions by ~0.72: an 8B model needs ~6.5 GB (fits on 8GB+ GPUs), a 32B model needs ~23–25 GB (fits on a 24GB RTX 4090/3090 or 32GB RTX 5090), and a 70B model needs ~46–50 GB (fits on Dual RTX 3090/5090 or a 64GB+ Mac Studio).
1. 2026 Local LLM VRAM Requirements Table (8B to 671B Across GGUF Quantizations)
Below is the exact total memory required (including static model weights + a 16,384-token Q8_0 KV cache + runtime overhead) across the most popular open-weight models in 2026:
| Model Size & Architecture | FP16 (16 bpw) | Q8_0 (8.5 bpw) | Q6_K (6.56 bpw) | Q4_K_M (4.85 bpw) | IQ3_XS (3.44 bpw) | Best Consumer Hardware Match (at Q4_K_M) |
|---|---|---|---|---|---|---|
| 8B Dense (Llama 3.2 / Qwen 3 8B) | 18.2 GB | 10.3 GB | 8.2 GB | 6.4 GB | 5.0 GB | Any 8GB–16GB GPU (RTX 4060 / 5070 / Mac 16GB) |
| 14B Dense (Qwen 3 14B / Phi-4) | 31.5 GB | 17.4 GB | 13.8 GB | 10.7 GB | 8.1 GB | 12GB–16GB GPU (RTX 4070 Ti / 5080 / Mac 24GB) |
| 32B Dense (Qwen 3 32B / Gemma 27B) | 70.8 GB | 38.5 GB | 30.2 GB | 23.1 GB | 17.4 GB | RTX 5090 (32GB) or RTX 3090/4090 (24GB) |
| 70B–72B Dense (Llama 3.3 / Qwen 72B) | 152.0 GB | 82.1 GB | 64.2 GB | 48.2 GB | 35.8 GB | Dual RTX 3090 (48GB), Dual 5090 (64GB), or Mac 64GB+ |
| 109B MoE (Llama 4 Scout · 17B Active) | 232.0 GB | 125.4 GB | 97.8 GB | 73.4 GB | 53.2 GB | Mac Studio 128GB or Dual RTX 5090 (at IQ3_XS) |
| 400B MoE (Llama 4 Maverick · 17B Active) | 840.0 GB | 452.0 GB | 351.0 GB | 262.0 GB | 188.0 GB | Mac Studio 512GB or Multi-Node GPU Cluster |
| 671B MoE (DeepSeek-R1 · 37B Active) | 1,390 GB | 750.0 GB | 582.0 GB | 436.0 GB | 310.0 GB (178 GB @ 1.58b) | Mac Studio 512GB (Q4) or 192GB Mac (at 1.58-bit Dynamic) |
2. NVIDIA RTX 5090 vs. RTX 4090 vs. Dual 3090 vs. Mac Studio: Speed & Value
When building a local AI workstation in 2026, you are balancing two hardware specs: VRAM Capacity (GB), which determines the largest model and context window you can load without slow PCIe RAM offloading, and Memory Bandwidth (GB/s), which directly determines how many tokens per second (tok/s) the hardware can stream:
- NVIDIA GeForce RTX 5090 (32 GB GDDR7 · 1,792 GB/s): The undisputed king of single-GPU speed in 2026 (NVIDIA Official Specs). Its 32 GB VRAM buffer finally fits 32B models at Q4_K_M or Q5_K_M with a full 32K–64K context window (plus 70B models at IQ3_XS) while delivering nearly 78% higher memory bandwidth than the RTX 4090 (1,792 GB/s vs. 1,008 GB/s).
- Dual Used NVIDIA RTX 3090 (48 GB Total VRAM · ~936 GB/s): At ~$700–$800 per used card (~$1,500 total), a dual RTX 3090 rig remains the highest-value budget build for running 70B–72B Q4_K_M models at ~25–30 tokens/sec via tensor splitting in `llama.cpp` or `ExLlamaV2`.
- Apple Mac Studio M4 Max (128 GB) & M3/M4 Ultra (192 GB–512 GB): While Apple Silicon has lower memory bandwidth (546 GB/s on M4 Max; 819 GB/s on Ultra) than an RTX 5090, its massive unified memory pool—and ~95W power draw—makes it the only quiet desktop machine that can load 109B to 671B MoE models entirely in memory for under $5,500.
Frequently Asked Questions: Local LLM VRAM & Quantization
Which GGUF quantization should I choose: Q4_K_M, Q5_K_M, Q6_K, or Q8_0?
For 90% of local LLM users, Q4_K_M (~4.85 bits/weight) is the optimal sweet spot—retaining over 98%–99% of FP16 benchmark accuracy while cutting VRAM consumption by 70%. If you have 4 to 6 GB of spare VRAM headroom on your GPU, step up to Q5_K_M (5.69 bpw) or Q6_K (6.56 bpw) for coding and math tasks. Going above Q8_0 (8.5 bpw) to FP16 doubles memory usage and halves your tokens/sec speed for virtually zero perceptible quality gain.
Why does my LLM slow down to a crawl when I increase the context window?
As your context window grows from 4K to 32K or 64K tokens, the KV cache expands linearly. The moment your combined Model Weights + KV Cache exceed your GPU’s physical VRAM (e.g., 24.0 GB on an RTX 4090), the inference engine spills layers or KV cache over the PCIe bus into system DDR5 RAM (~60–90 GB/s instead of 1,008–1,792 GB/s), causing token generation speed to drop by 70%–85%. Enable Q8_0 KV cache quantization to immediately reclaim half of your context VRAM.
How much Unified Memory can macOS allocate to the GPU for local LLMs?
By default, macOS allows Metal to allocate roughly 75% of total Unified Memory as wired GPU memory (for example, ~96 GB on a 128 GB Mac Studio), leaving the rest for the OS. Advanced users can raise this ceiling to ~85%–90% using the terminal command
sudo sysctl iogpu.wired_limit_mb=....Is it cheaper to buy an RTX 5090 or rent GPUs on RunPod / Vast.ai?
If you run local LLMs or fine-tuning jobs for less than 1 to 2 hours per day, renting an RTX 5090 ($0.69/hr) or RTX 4090 ($0.44/hr) on demand in the cloud costs just $15 to $40/month. Once you use local AI for 3.5+ hours per day (or need 24/7 always-on local coding/agent privacy), buying a local GPU rig breaks even in 12 to 16 months after accounting for hardware resale value.
Official Hardware & LLM Sizing Primary Sources
- NVIDIA Official Specifications: GeForce RTX 5090 (32 GB GDDR7 · 512-bit · 1,792 GB/s Bandwidth)
- RunPod AI Hardware Guide: NVIDIA RTX 5090 32GB VRAM & LLM Inference Benchmarks
- ApX Machine Learning: LLM VRAM & KV Cache Memory Formulas
- PromptQuorum: Local LLM VRAM Sizing & Quantization Reference (2026)
- Spheron Network: Llama 4 Scout & Maverick MoE VRAM Requirements Guide
Explore More AI, Developer & Creator Economics Calculators
⚠️ Hardware & Benchmark Disclaimer: This calculator provides engineering estimates based on standard GGUF/EXL2/AWQ bits-per-weight formulas, Grouped-Query Attention (GQA) KV cache math, and published memory bandwidth specifications current as of October 2026. Actual VRAM usage and token generation speeds vary by inference backend (`llama.cpp`, `Ollama`, `vLLM`, `ExLlamaV2`, `MLX`), flash-attention settings, prompt batch size, and OS display memory overhead.
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