Karna 49M SFT Model — Hardware Requirements & GPU Compatibility
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Karna 49M SFT Model is a 49M-parameter open language model from VarunHujan. At Q4_K_M it needs about 0.03 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 · ~21840 tok/s · 32K)Macs from 8 GB (Q4_K_M · ~2389 tok/s)
MIT
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run Karna 49M SFT Model
One recommended way. Every other method, OS and quant is in the step-by-step guide.Each way opens a step-by-step guide for your OS and quant.
Recommended
vllm serve VarunHujan/Karna-49M-SFT-Model --max-model-len 16384vLLM repo with its own quantisation
Download: Weights on Hugging Face
Ways to runOther ways
each opens step-by-stepRun in cloud
Runs on RTX 3060 12GB (11 GB headroom) · Q4_K_M
- Generation speed
- ~7800 tok/s
- generation speed
- Cost per 1M output tokens
- $0.0022
- per 1M output tokens
How Much VRAM Does Karna 49M SFT Model Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 0.0 GB | 1.3 GB | 2-bit · K-quant — 2-bit quantization with K-quant improvements | Fits: 32K context · ~32760 tok/s | |
| 0.0 GB | 1.4 GB | 4-bit · legacy — 4-bit legacy quantization | Fits: 32K context · ~21840 tok/s | |
| 0.0 GB | 1.4 GB | 4-bit medium — 4-bit medium quantization — most popular sweet spot | Fits: 32K context · ~21840 tok/s | |
| 0.0 GB | 1.4 GB | 3-bit medium — 3-bit medium quantization | Fits: 32K context · ~21840 tok/s | |
| 0.0 GB | 1.4 GB | 6-bit, very good — 6-bit quantization, very good quality | Fits: 32K context · ~16380 tok/s | |
| 0.0 GB | 1.4 GB | 5-bit medium — 5-bit medium quantization — good quality/size tradeoff | Fits: 32K context · ~16380 tok/s | |
| 0.1 GB | 1.4 GB | 8-bit, near-lossless — 8-bit quantization, near-lossless | Fits: 32K context · ~13104 tok/s | |
| 0.1 GB | 1.4 GB | 16-bit · bfloat — Brain floating point 16 — preferred for training | Fits: 32K context · ~5956 tok/s |
Selected Q4_K_M
4-bit medium quantization0.7GBat 16K context
- Weights
- 0.1 GB
- KV cache
- 0.3 GB
- Overhead
- 0.3 GB
Fits RTX 4090 (reference) · 23.4 GB free · ~21840 tok/s
≥50 tok/s needs ≥2 GB/s · RTX 5090 ~38827, RTX 3090 Ti ~21840
Which Hardware Can Run Karna 49M SFT Model?
Q4_K_M · 0.0 GBKarna 49M SFT Model (Q4_K_M) requires 0.0 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 1+ GB is recommended. 52 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti. 163 devices with unified memory can run Karna 49M SFT Model, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB).
≤ 11 GB
≤ 11 GB tier →fits · 7.3 GB free · up to 32K ctx
16 GPUs · 1 Mac class · 2 device families
RTX 3080~16473Simulate output on RTX 3080RTX 2080 Ti~13347Simulate output on RTX 2080 TiRTX 3070 Ti~13180Simulate output on RTX 3070 TiM3*8 GB~2389Simulate output on iMac 24" M3 (8 GB)Orin Nano~2210Simulate output on NVIDIA Jetson Orin Nano 8GB (Super)iPhone~1591Simulate output on iPhone 1720 GB
fits · 19.4 GB free · up to 32K ctx
1 GPU
RX 7900 XT~16000Simulate output on RX 7900 XT32–40 GB
fits · 31.4 GB free · up to 32K ctx
1 GPU · 3 Mac classes · 4 device families
RTX 5090~38827Simulate output on RTX 5090M5 Max36 GB~10733Simulate output on Mac Studio M5 Max (36 GB)M532 GB~3584Simulate output on MacBook Air 13" M5 (32 GB)Orin~4437Simulate output on NVIDIA Jetson AGX Orin 32GBSnapdragon X Elite (X1E-80-100)~2925Simulate output on Snapdragon X Elite Copilot+ PC
By VRAM tier · typical GPUs and Macs at Q4_K_M
Frequently Asked Questions
How much VRAM does Karna 49M SFT Model need?
Karna 49M SFT Model requires 0.0 GB of VRAM at Q4_K_M, or 0.1 GB at BF16.
VRAM = Weights + KV Cache + Overhead
Weights = 49M × 4.8 bits ÷ 8 = 0 GB
Fit ratings and hardware model lists check this model with room for a 16K-token context, which needs a little more memory.
VRAM usage by quantization
What's the best quantization for Karna 49M SFT Model?
For Karna 49M SFT Model, Q4_K_M (0.0 GB) offers the best balance of quality and VRAM usage. Q6_K (0.0 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 0.0 GB.
VRAM requirement by quantization
★ Recommended — best balance of quality and VRAM usage.
Can I run Karna 49M SFT Model on a Mac?
Yes — MacBook Air 13" M3 (8 GB) and 145 other Macs can run Karna 49M SFT Model. Apple Silicon uses unified memory, so the model shares RAM with the system. At Q4_K_M you need at least 0.0 GB of usable unified memory (RAM minus macOS overhead).
Can I run Karna 49M SFT Model locally?
Yes — Karna 49M SFT Model can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.
How fast is Karna 49M SFT Model?
At Q4_K_M, Karna 49M SFT Model can reach ~160000 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~21840 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 ÷ 0.03 × 0.65 = ~173333 tok/s
Estimated speed at Q4_K_M (0.0 GB)
Real-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
What's the download size of Karna 49M SFT Model?
At Q4_K_M, the download is about 0.03 GB. The full-precision BF16 version is 0.10 GB. The smallest option (Q2_K) is 0.02 GB.
Which GPUs can run Karna 49M SFT Model?
52 consumer GPUs can run Karna 49M SFT Model 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 Karna 49M SFT Model?
163 devices with unified memory can run Karna 49M SFT Model at Q4_K_M (0.0 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.