RTX 3060 12GBBudget pick12 GB VRAM (+4)360 GB/s (+104)
C
Raises estimated decode speed by about 27%.55.6 tok/s decode
Raises estimated decode speed by about 27%.
Adds memory headroom for longer context windows and future model growth.
~$329 MSRP
baichuan2 7b chat needs ~7.1 GB VRAM. RTX 2000 Ada Laptop 8GB has 8.0 GB. With Q4_K_M quantization, expect ~44 tok/s.
Operating mode
Interactive favors responsiveness, while light API and scale-out lean harder on serving readiness. The fit stays the same, but the recommendation lens changes.
Current mode
Balanced
Balanced for general local use. Keeps the ranking neutral across personal and serving workflows.
Select quantization to explore
Fit status
Tight fit
Decode
43.8 tok/s
TTFT
4424 ms
Safe context
34K
Memory
7.1 GB / 8.0 GB
This setup is broadly balanced for this model.
No major red flags
This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Tight fit | 43.8 tok/s | 2413 ms | 34K |
| Coding | C | Tight fit | 43.8 tok/s | 4424 ms | 34K |
| Agentic Coding | C | Runs with offload | 43.8 tok/s | 6434 ms | 34K |
| Reasoning | C | Tight fit | 43.8 tok/s | 5228 ms | 34K |
| RAG | C | Runs with offload | 43.8 tok/s | 8043 ms | 34K |
Inference speed
Estimated decode speed (tokens/sec) for baichuan2 7b chat at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~98 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.
| GPU / Mac | Memory | Quant | Speed (tok/s) | Fits? |
|---|---|---|---|---|
| RTX 5090 32GB | 32 GB | Q4_K_M | 98.0 | Fits |
| RTX 4090 24GB | 24 GB | Q4_K_M | 98.0 | Fits |
| RTX 4080 Super 16GB | 16 GB | Q4_K_M | 98.0 | Fits |
| RTX 3090 24GB | 24 GB | Q4_K_M | 98.0 | Fits |
RX 7900 XTX 24GB | 24 GB | Q4_K_M | 98.0 | Fits |
Mac Studio M3 Ultra 256GB | 256 GB | Q4_K_M | 98.0 | Fits |
Mac Studio M2 Ultra 128GB | 128 GB | Q4_K_M | 98.0 | Fits |
Mac Studio M1 Ultra 128GB | 128 GB | Q4_K_M | 98.0 | Fits |
| RTX 4070 12GB | 12 GB | Q4_K_M | 88.5 | Fits |
MacBook Pro M4 Max 128GB | 128 GB | Q4_K_M | 87.8 | Fits |
MacBook Pro M4 Max 64GB | 64 GB | Q4_K_M | 87.8 | Fits |
MacBook Pro M3 Max 64GB | 64 GB | Q4_K_M | 56.2 | Fits |
| RTX 3060 12GB | 12 GB | Q4_K_M | 55.6 | Fits |
MacBook Pro M1 Max 64GB | 64 GB | Q4_K_M | 51.5 | Fits |
| RTX 4060 8GB | 8 GB | Q4_K_M | 46.5 | Tight |
MacBook Pro M4 Pro 48GB | 48 GB | Q4_K_M | 45.3 | Fits |
Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.
How baichuan2 7b chat (7B params) fits at each quantization level on RTX 2000 Ada Laptop 8GB (8.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 2.7 GB | Low | C53 |
Q3_K_S | 3 | 3.4 GB | Low | C53 |
NVFP4 | 4 | 3.9 GB | Medium | C53 |
Q4_K_M | 4 | 4.3 GB | Medium | C53 |
Q5_K_MBest for your GPU | 5 | 5.0 GB | High | C52 |
Q6_K | 6 | 5.7 GB | High | F0 |
Q8_0 | 8 | 7.5 GB | Very High | F0 |
F16 | 16 | 14.3 GB | Maximum | F0 |
Copy-paste commands to run baichuan2 7b chat on your machine.
Run
lms load hf-shaowenchen--baichuan2-7b-chat-gguf && lms server startUpgrade options
RTX 3060 12GBBudget pickRaises estimated decode speed by about 27%.
Adds memory headroom for longer context windows and future model growth.
~$329 MSRP
RTX 5070 12GBBest valueRaises estimated decode speed by about 124%.
Adds memory headroom for longer context windows and future model growth.
~$549 MSRP
RTX 4070 Super 12GBNVIDIA upgradeRaises estimated decode speed by about 108%.
Adds memory headroom for longer context windows and future model growth.
~$599 MSRP
Yes, RTX 2000 Ada Laptop 8GB can run baichuan2 7b chat with a C grade (Tight fit). Expected decode speed: 43.8 tok/s.
baichuan2 7b chat (7B parameters) requires approximately 7.1 GB of memory with Q4_K_M quantization.
The recommended quantization for baichuan2 7b chat is Q4_K_M, which balances quality and memory efficiency.
On RTX 2000 Ada Laptop 8GB, baichuan2 7b chat achieves approximately 43.8 tokens per second decode speed with a time-to-first-token of 4424ms using Q4_K_M quantization.
For coding workloads, baichuan2 7b chat on RTX 2000 Ada Laptop 8GB receives a C grade with 43.8 tok/s and 34K context.
On RTX 2000 Ada Laptop 8GB, baichuan2 7b chat can safely use up to 34K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
Paste this snippet into any page to show a live fit card.
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