SemanticRepair 270M — Hardware Requirements & GPU Compatibility
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SemanticRepair 270M is a 268M-parameter open language model from Gramscii-IT. It supports a context window of up to 32,768 tokens. At Q4_K_M it needs about 0.48 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 · 32K)Best on 24 GB (Q4_K_M · ~1365 tok/s · 32K)Macs from 8 GB (Q4_K_M · ~149 tok/s)
Gemma Terms
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run SemanticRepair 270M
One recommended way. Every other method, OS and quant is in the step-by-step guide.
Recommended
vllm serve Gramscii-IT/SemanticRepair-270M --max-model-len 16384vLLM repo with its own quantisation
Download: Weights on Hugging Face
Other ways
each opens step-by-stepRun in cloud
Fits on RTX 3060 12GB (11 GB headroom) · Q4_K_M
- Generation speed
- ~488 tok/s
- generation speed
- Cost per 1M output tokens
- $0.040
- per 1M output tokens
How Much VRAM Does SemanticRepair 270M Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 0.4 GB | 0.5 GB | 2-bit · K-quant — 2-bit quantization with K-quant improvements | Fits: 32K context · ~1489 tok/s | |
| 0.5 GB | 0.5 GB | 3-bit medium — 3-bit medium quantization | Fits: 32K context · ~1456 tok/s | |
| 0.5 GB | 0.6 GB | 4-bit medium — 4-bit medium quantization — most popular sweet spot | Fits: 32K context · ~1365 tok/s | |
| 0.5 GB | 0.6 GB | 5-bit medium — 5-bit medium quantization — good quality/size tradeoff | Fits: 32K context · ~1285 tok/s | |
| 0.5 GB | 0.6 GB | 6-bit, very good — 6-bit quantization, very good quality | Fits: 32K context · ~1213 tok/s | |
| 0.6 GB | 0.7 GB | 8-bit, near-lossless — 8-bit quantization, near-lossless | Fits: 32K context · ~1111 tok/s | |
| 0.9 GB | 0.9 GB | 16-bit · bfloat — Brain floating point 16 — preferred for training | Fits: 32K context · ~762 tok/s |
Selected Q4_K_M
4-bit medium quantization0.5GBat 16K context
- Weights
- 0.2 GB
- KV cache
- 0.1 GB
- Overhead
- 0.3 GB
Fits RTX 4090 (reference) · 23.5 GB free · ~1365 tok/s
≥50 tok/s needs ≥37 GB/s · RTX 5090 ~2427, RTX 3090 Ti ~1365
Which Hardware Can Run SemanticRepair 270M?
Q4_K_M · 0.5 GBSemanticRepair 270M (Q4_K_M) requires 0.5 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 1+ GB is recommended. Using the full 33K context window can add up to 0.1 GB, bringing total usage to 0.6 GB. 52 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti. 163 devices with unified memory can run SemanticRepair 270M, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB).
≤ 11 GB
≤ 11 GB tier →fits · 7.5 GB free · up to 32K ctx
16 GPUs · 1 Mac class · 2 device families
RTX 3080~1030Simulate output on RTX 3080RTX 2080 Ti~834Simulate output on RTX 2080 TiRTX 3070 Ti~824Simulate output on RTX 3070 TiM3*8 GB~149Simulate output on iMac 24" M3 (8 GB)Orin Nano~138Simulate output on NVIDIA Jetson Orin Nano 8GB (Super)iPhone~100Simulate output on iPhone 1720 GB
fits · 19.5 GB free · up to 32K ctx
1 GPU
RX 7900 XT~1000Simulate output on RX 7900 XT32–40 GB
fits · 31.5 GB free · up to 32K ctx
1 GPU · 3 Mac classes · 4 device families
RTX 5090~2427Simulate output on RTX 5090M5 Max36 GB~671Simulate output on Mac Studio M5 Max (36 GB)M532 GB~224Simulate output on MacBook Air 13" M5 (32 GB)Orin~277Simulate output on NVIDIA Jetson AGX Orin 32GBSnapdragon X Elite (X1E-80-100)~183Simulate output on Snapdragon X Elite Copilot+ PC
By VRAM tier · typical GPUs and Macs at Q4_K_M
Similar models
2| Model | Score | VRAM | tok/s | Why it is similar |
|---|---|---|---|---|
| Gemma 3 270M ITGoogle | — | 1.2 GB | ~3640 | same size |
| Functiongemma 270M ITGoogle | — | 1.6 GB | ~1111 | same size |
Specifications
- Publisher
- Gramscii-IT
- Parameters
- 268M
- Architecture
- Gemma3ForCausalLM
- Context Length
- 32,768 tokens
- Vocabulary Size
- 262,144
- Release Date
- 2026-08-22
- License
- Gemma Terms
Frequently Asked Questions
How much VRAM does SemanticRepair 270M need?
SemanticRepair 270M requires 0.5 GB of VRAM at Q4_K_M, or 0.9 GB at BF16.
VRAM = Weights + KV Cache + Overhead
Weights = 268M × 4.8 bits ÷ 8 = 0.2 GB
KV Cache + Overhead ≈ 0.3 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 ≈ 0.4 GB (at full 33K context)
VRAM usage by quantization
What's the best quantization for SemanticRepair 270M?
For SemanticRepair 270M, Q4_K_M (0.5 GB) offers the best balance of quality and VRAM usage. Q5_K_M (0.5 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 0.4 GB.
VRAM requirement by quantization
★ Recommended — best balance of quality and VRAM usage.
Can I run SemanticRepair 270M on a Mac?
Yes — MacBook Air 13" M3 (8 GB) and 145 other Macs can run SemanticRepair 270M. Apple Silicon uses unified memory, so the model shares RAM with the system. At Q4_K_M you need at least 0.5 GB of usable unified memory (RAM minus macOS overhead).
Can I run SemanticRepair 270M locally?
Yes — SemanticRepair 270M can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.
How fast is SemanticRepair 270M?
At Q4_K_M, SemanticRepair 270M can reach ~10000 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~1365 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 ÷ 0.48 × 0.65 = ~10833 tok/s
Estimated speed at Q4_K_M (0.5 GB)
Real-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
What's the download size of SemanticRepair 270M?
At Q4_K_M, the download is about 0.16 GB. The full-precision BF16 version is 0.54 GB. The smallest option (Q2_K) is 0.11 GB.
Which GPUs can run SemanticRepair 270M?
52 consumer GPUs can run SemanticRepair 270M 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 SemanticRepair 270M?
163 devices with unified memory can run SemanticRepair 270M at Q4_K_M (0.5 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.