TwIL LM3 — Hardware Requirements & GPU Compatibility
ChatReasoningFunctions
TwIL LM3 is a 3.1B-parameter open language model from webAI-Official. It supports a context window of up to 65,536 tokens. At Q4_K_M it needs about 2.30 GB of VRAM — see which GPUs and Macs can run it below.
Runs great on a 24 GB GPU
Runs from 8 GB (Q4_K_M · 64K)Best on 24 GB (Q4_K_M · ~285 tok/s · 64K)Macs from 8 GB (Q4_K_M · ~31 tok/s)
Other
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run TwIL LM3
One recommended way. Every other method, OS and quant is in the step-by-step guide.
Recommended
ollama run hf.co/NANI-Nithin/TwIL-LM3-GGUF:Q4_K_MQ4_K_M fits fully on the RTX 4090 · 20.6 GB free · 16K context
Download: twil-lm3-q4_k_m.gguf1.92 GBNANI-Nithin
Other ways
each opens step-by-stepRun in cloud
Fits on RTX 3060 12GB (8 GB headroom) · Q4_K_M
- Generation speed
- ~102 tok/s
- generation speed
- Cost per 1M output tokens
- $0.19
- per 1M output tokens
How Much VRAM Does TwIL LM3 Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 1.8 GB | 4 GB | 2-bit · K-quant — 2-bit quantization with K-quant improvements | Fits: 64K context · ~372 tok/s | |
| 1.8 GB | 4.1 GB | 3-bit small — 3-bit small quantization | Fits: 64K context · ~364 tok/s | |
| 1.9 GB | 4.2 GB | 3-bit medium — 3-bit medium quantization | Fits: 64K context · ~336 tok/s | |
| 2.0 GB | 4.3 GB | 4-bit · legacy — 4-bit legacy quantization | Fits: 64K context · ~329 tok/s | |
| 2.3 GB | 4.6 GB | 4-bit medium — 4-bit medium quantization — most popular sweet spot | Fits: 64K context · ~285 tok/s | |
| 2.6 GB | 4.9 GB | 5-bit medium — 5-bit medium quantization — good quality/size tradeoff | Fits: 64K context · ~248 tok/s | |
| 3.0 GB | 5.3 GB | 6-bit, very good — 6-bit quantization, very good quality | Fits: 64K context · ~219 tok/s | |
| 3.5 GB | 5.8 GB | 8-bit, near-lossless — 8-bit quantization, near-lossless | Fits: 64K context · ~186 tok/s |
Selected Q4_K_M
4-bit medium quantization3.4GBat 16K context
- Weights
- 1.9 GB
- KV cache
- 1.2 GB
- Overhead
- 0.3 GB
Fits RTX 4090 (reference) · 20.6 GB free · ~285 tok/s
≥50 tok/s needs ≥177 GB/s · RTX 5090 ~506, RTX 3090 Ti ~285
Which Hardware Can Run TwIL LM3?
Q4_K_M · 2.3 GBTwIL LM3 (Q4_K_M) requires 2.3 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 3+ GB is recommended. Using the full 66K context window can add up to 4.7 GB, bringing total usage to 7.0 GB. 52 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti. 163 devices with unified memory can run TwIL LM3, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB).
≤ 11 GB
≤ 11 GB tier →fits · 4.6 GB free · up to 64K ctx
16 GPUs · 1 Mac class · 2 device families
RTX 3080~215Simulate output on RTX 3080RTX 2080 Ti~174Simulate output on RTX 2080 TiRTX 3070 Ti~172Simulate output on RTX 3070 TiM3*8 GB~31Simulate output on iMac 24" M3 (8 GB)Orin Nano~29Simulate output on NVIDIA Jetson Orin Nano 8GB (Super)iPhone~21Simulate output on iPhone 1720 GB
fits · 16.6 GB free · up to 64K ctx
1 GPU
RX 7900 XT~209Simulate output on RX 7900 XT32–40 GB
fits · 28.6 GB free · up to 64K ctx
1 GPU · 3 Mac classes · 4 device families
RTX 5090~506Simulate output on RTX 5090M5 Max36 GB~140Simulate output on Mac Studio M5 Max (36 GB)M532 GB~47Simulate output on MacBook Air 13" M5 (32 GB)Orin~58Simulate output on NVIDIA Jetson AGX Orin 32GBSnapdragon X Elite (X1E-80-100)~38Simulate output on Snapdragon X Elite Copilot+ PC
By VRAM tier · typical GPUs and Macs at Q4_K_M
Similar models
5| Model | Score | VRAM | tok/s | Why it is similar |
|---|---|---|---|---|
| SmolLM3 3BHugging Face | — | 3.4 GB | ~285 | same size |
| Llama 3.2 3B InstructMeta | — | 4.1 GB | ~309 | same size |
| AI21 Jamba Reasoning 3BAI21 Labs | — | 2.3 GB | ~291 | same size |
| Qwen2.5 3B InstructAlibaba | — | 2.8 GB | ~294 | same size |
| MiniMax M3 EAGLE3Inferact | — | 2.7 GB | ~284 | same size |
Specifications
- Publisher
- webAI-Official
- Parameters
- 3.1B
- Architecture
- SmolLM3ForCausalLM
- Context Length
- 65,536 tokens
- Vocabulary Size
- 128,256
- Release Date
- 2026-08-03
- License
- Other
Frequently Asked Questions
How much VRAM does TwIL LM3 need?
TwIL LM3 requires 2.3 GB of VRAM at Q4_K_M, or 6.6 GB at BF16. Full 66K context adds up to 4.7 GB (7.0 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 3.1B × 4.8 bits ÷ 8 = 1.8 GB
KV Cache + Overhead ≈ 0.5 GB (at 2K context + ~0.3 GB framework)
Fit ratings and hardware model lists check this model with room for a 16K-token context, which needs a little more memory.
KV Cache + Overhead ≈ 5.2 GB (at full 66K context)
VRAM usage by quantization
What's the best quantization for TwIL LM3?
For TwIL LM3, Q4_K_M (2.3 GB) offers the best balance of quality and VRAM usage. Q5_K_S (2.6 GB) provides better quality if you have the VRAM. The smallest option is IQ2_M at 1.5 GB.
VRAM requirement by quantization
★ Recommended — best balance of quality and VRAM usage.
Can I run TwIL LM3 on a Mac?
Yes — MacBook Air 13" M3 (8 GB) and 145 other Macs can run TwIL LM3. Apple Silicon uses unified memory, so the model shares RAM with the system. At Q4_K_M you need at least 2.3 GB of usable unified memory (RAM minus macOS overhead).
Can I run TwIL LM3 locally?
Yes — TwIL LM3 can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.
How fast is TwIL LM3?
At Q4_K_M, TwIL LM3 can reach ~2087 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~285 tok/s. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.
tok/s = (bandwidth GB/s ÷ model GB) × efficiency
Example: NVIDIA B200 → 8000 ÷ 2.3 × 0.65 = ~2261 tok/s
Estimated speed at Q4_K_M (2.3 GB)
Real-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
What's the download size of TwIL LM3?
At Q4_K_M, the download is about 1.85 GB. The full-precision BF16 version is 6.15 GB. The smallest option (IQ2_M) is 1.04 GB.
Which GPUs can run TwIL LM3?
52 consumer GPUs can run TwIL LM3 at Q4_K_M. Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT. 52 GPUs have plenty of headroom for comfortable inference.
Which devices can run TwIL LM3?
163 devices with unified memory can run TwIL LM3 at Q4_K_M (2.3 GB), including AMD Ryzen AI 9 HX 370 (Strix Point) Laptop, ASUS Ascent GX10, Beelink GTR9 Pro (Ryzen AI Max+ 395, 128 GB), Framework Desktop (Ryzen AI Max+ 395, 128 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.