YanoljaNEXT EEVE Instruct 2.8B — Hardware Requirements & GPU Compatibility
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YanoljaNEXT EEVE Instruct 2.8B is a 2.8B-parameter open language model from yanolja. It supports a context window of up to 2,048 tokens. At Q4_K_M it needs about 2.66 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 · 2K)Best on 24 GB (Q4_K_M · ~246 tok/s · 2K)Macs from 8 GB (Q4_K_M · ~27 tok/s)
Apache 2.0
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run YanoljaNEXT EEVE Instruct 2.8B
One recommended way. Every other method, OS and quant is in the step-by-step guide.
Recommended
vllm serve yanolja/YanoljaNEXT-EEVE-Instruct-2.8B --max-model-len 2048vLLM repo with its own quantisation
Download: Weights on Hugging Face
Other ways
each opens step-by-stepRun in cloud
Fits on RTX 3060 12GB (9 GB headroom) · Q4_K_M
- Generation speed
- ~88 tok/s
- generation speed
- Cost per 1M output tokens
- $0.22
- per 1M output tokens
How Much VRAM Does YanoljaNEXT EEVE Instruct 2.8B Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 2.2 GB | — | 2-bit · K-quant — 2-bit quantization with K-quant improvements | Fits: 2K context · ~302 tok/s | |
| 2.4 GB | — | 3-bit medium — 3-bit medium quantization | Fits: 2K context · ~279 tok/s | |
| 2.7 GB | — | 4-bit medium — 4-bit medium quantization — most popular sweet spot | Fits: 2K context · ~246 tok/s | |
| 3.0 GB | — | 5-bit medium — 5-bit medium quantization — good quality/size tradeoff | Fits: 2K context · ~220 tok/s | |
| 3.3 GB | — | 6-bit, very good — 6-bit quantization, very good quality | Fits: 2K context · ~199 tok/s | |
| 3.8 GB | — | 8-bit, near-lossless — 8-bit quantization, near-lossless | Fits: 2K context · ~173 tok/s | |
| 6.6 GB | — | 16-bit · bfloat — Brain floating point 16 — preferred for training | Fits: 2K context · ~99 tok/s |
Selected Q4_K_M
4-bit medium quantization2.7GBat 2K context
- Weights
- 1.7 GB
- KV cache
- 0.7 GB
- Overhead
- 0.3 GB
Fits RTX 4090 (reference) · 21.3 GB free · ~246 tok/s
≥50 tok/s needs ≥204 GB/s · RTX 5090 ~438, RTX 3090 Ti ~246
Which Hardware Can Run YanoljaNEXT EEVE Instruct 2.8B?
Q4_K_M · 2.7 GBYanoljaNEXT EEVE Instruct 2.8B (Q4_K_M) requires 2.7 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 4+ 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 YanoljaNEXT EEVE Instruct 2.8B, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB).
≤ 11 GB
≤ 11 GB tier →fits · 5.3 GB free · up to 2K ctx
16 GPUs · 1 Mac class · 2 device families
RTX 3080~186Simulate output on RTX 3080RTX 2080 Ti~151Simulate output on RTX 2080 TiRTX 3070 Ti~149Simulate output on RTX 3070 TiM3*8 GB~27Simulate output on iMac 24" M3 (8 GB)Orin Nano~25Simulate output on NVIDIA Jetson Orin Nano 8GB (Super)iPhone~18Simulate output on iPhone 1720 GB
fits · 17.3 GB free · up to 2K ctx
1 GPU
RX 7900 XT~181Simulate output on RX 7900 XT32–40 GB
fits · 29.3 GB free · up to 2K ctx
1 GPU · 3 Mac classes · 4 device families
RTX 5090~438Simulate output on RTX 5090M5 Max36 GB~121Simulate output on Mac Studio M5 Max (36 GB)M532 GB~40Simulate output on MacBook Air 13" M5 (32 GB)Orin~50Simulate output on NVIDIA Jetson AGX Orin 32GBSnapdragon X Elite (X1E-80-100)~33Simulate output on Snapdragon X Elite Copilot+ PC
By VRAM tier · typical GPUs and Macs at Q4_K_M
Similar models
6| Model | Score | VRAM | tok/s | Why it is similar |
|---|---|---|---|---|
| LFM2.5 2.6BLiquid AI | — | 2.3 GB | ~321 | same size |
| Granite 3.0 2B InstructIBM | — | 2.2 GB | ~320 | same size |
| Gemma 2 2B ITGoogle | — | 2.5 GB | ~379 | same size |
| Phi 2Microsoft | — | 2.6 GB | ~248 | same size |
| Nemotron Flash 3BNVIDIA | — | 7.6 GB | ~109 | same size |
| Stablelm 3B 4e1tStability AI | — | 3.3 GB | ~247 | same size |
Specifications
- Publisher
- yanolja
- Parameters
- 2.8B
- Architecture
- PhiForCausalLM
- Context Length
- 2,048 tokens
- Vocabulary Size
- 58,944
- Release Date
- 2024-02-22
- License
- Apache 2.0
Frequently Asked Questions
How much VRAM does YanoljaNEXT EEVE Instruct 2.8B need?
YanoljaNEXT EEVE Instruct 2.8B requires 2.7 GB of VRAM at Q4_K_M, or 6.6 GB at BF16.
VRAM = Weights + KV Cache + Overhead
Weights = 2.8B × 4.8 bits ÷ 8 = 1.7 GB
KV Cache + Overhead ≈ 1 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.
VRAM usage by quantization
What's the best quantization for YanoljaNEXT EEVE Instruct 2.8B?
For YanoljaNEXT EEVE Instruct 2.8B, Q4_K_M (2.7 GB) offers the best balance of quality and VRAM usage. Q5_K_M (3.0 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 2.2 GB.
VRAM requirement by quantization
★ Recommended — best balance of quality and VRAM usage.
Can I run YanoljaNEXT EEVE Instruct 2.8B on a Mac?
Yes — MacBook Air 13" M3 (8 GB) and 145 other Macs can run YanoljaNEXT EEVE Instruct 2.8B. Apple Silicon uses unified memory, so the model shares RAM with the system. At Q4_K_M you need at least 2.7 GB of usable unified memory (RAM minus macOS overhead).
Can I run YanoljaNEXT EEVE Instruct 2.8B locally?
Yes — YanoljaNEXT EEVE Instruct 2.8B can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.
How fast is YanoljaNEXT EEVE Instruct 2.8B?
At Q4_K_M, YanoljaNEXT EEVE Instruct 2.8B can reach ~1805 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~246 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 ÷ 2.66 × 0.65 = ~1955 tok/s
Estimated speed at Q4_K_M (2.7 GB)
Real-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
What's the download size of YanoljaNEXT EEVE Instruct 2.8B?
At Q4_K_M, the download is about 1.69 GB. The full-precision BF16 version is 5.64 GB. The smallest option (Q2_K) is 1.20 GB.
Which GPUs can run YanoljaNEXT EEVE Instruct 2.8B?
52 consumer GPUs can run YanoljaNEXT EEVE Instruct 2.8B 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 YanoljaNEXT EEVE Instruct 2.8B?
163 devices with unified memory can run YanoljaNEXT EEVE Instruct 2.8B at Q4_K_M (2.7 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.