RTX 3060 12GB经济之选12 GB VRAM (+1)
C
Removes host-memory offload, which is usually the single biggest latency and throughput win.19.5 tok/s 解码
Removes host-memory offload, which is usually the single biggest latency and throughput win.
~$329 MSRP
Can GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV run on RTX 2080 Ti 11GB?
BARELY — Tight on Memory
D40Poor
Estimated from fit model
GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV needs ~12.2 GB VRAM. RTX 2080 Ti 11GB has 11.0 GB. With Q4_K_M quantization, expect ~27 tok/s.
Runtime: llama.cppCapacity: OffloadBandwidth: MediumStack: StandardBottleneck: Host offload
Operating mode
Choose the run profile you care about
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
Q4_K_M (Medium quality) — 12.2 GB, 27.3 tok/s, Very compromised (needs ~0.8 GB host RAM)
12.2 GB required11.0 GB available
111% VRAM needed
1.2 GB over capacity — needs offload or smaller quantization
Fit status
Very compromised (needs ~0.8 GB host RAM)
Decode
27.3 tok/s
TTFT
7082 ms
Safe context
4K
Memory
12.2 GB / 11.0 GB
Offload
10%
Memory breakdown
Weights8.5 GB
KV Cache1.6 GB
Runtime0.9 GB
Headroom1.1 GB
See how fast it feels
See how fast it feelsGGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV on RTX 2080 Ti 11GB
1st promptCold start — includes initialization
>What is local AI inference?
Local AI inference means running an AI language model directly on your own hardware — your laptop, desktop, or server — instead of sending requests to a remote cloud API.
When you run inference locally the model weights are loaded into your GPU or unified memory. Each token you generate requires reading those weights from memory, so memory bandwidth is the main bottleneck for decode speed.
Key benefits of running locally:
- Full privacy: your prompts never leave your machine
- No per-token cost or rate limits
- Works offline once the model is downloaded
- Latency depends only on your hardware
2nd promptWarm — model ready, no init overhead
>How much VRAM do I need?
It depends on the model size and quantization level. A rough rule of thumb:
Model size Q4 (4-bit) Q8 (8-bit) FP16
7B params ~4.3 GB ~7.5 GB ~14 GB
13B params ~7.9 GB ~13.9 GB ~26 GB
70B params ~42.7 GB ~74.9 GB ~140 GB
Most people use 4-bit quantization (Q4_K_M) which gives 90-95% of full quality at a fraction of the memory. A 24 GB GPU can comfortably run most 7B-13B models.
Estimated: 27.3 tok/s decode · 7.1s TTFT (warm) · 68 tok/s prefill
What limits this setup
It fits through host-memory offload, and offload is the main reason performance drops.
CPU or host-memory offload is active
About 10% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.
Very little memory headroom
You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.
Older PCIe generation
PCIe 3.0 is workable, but it compounds the penalty when you offload heavily or try to scale across multiple cards.
Best improvement path
Remove offload with more accelerator memory
Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
Increase host RAM if you keep offloading
This setup may need roughly 0.8 GB of extra host RAM just for the offloaded portion, before OS and other tools.
Performance by workload
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Runs with offload (needs ~0.3 GB host RAM) | 31.9 tok/s | 3314 ms | 4K |
| Coding | D | Very compromised (needs ~0.8 GB host RAM) | 27.3 tok/s | 7082 ms | 4K |
| Agentic Coding | F | Too heavy | 20.7 tok/s | 13595 ms | 4K |
| Reasoning | D | Very compromised (needs ~0.8 GB host RAM) | 27.3 tok/s | 8369 ms | 4K |
| RAG | F | Too heavy | 20.7 tok/s | 16993 ms | 4K |
Quantization options
How GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV (14B params) fits at each quantization level on RTX 2080 Ti 11GB (11.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 5.5 GB | Low | C52 |
Q3_K_S | 3 | 6.9 GB | Low | C52 |
NVFP4Best for your GPU | 4 | 7.8 GB | Medium | C51 |
Q4_K_M | 4 | 8.5 GB | Medium | F0 |
Q5_K_M | 5 | 10.1 GB | High | F0 |
Q6_K | 6 | 11.5 GB | High | F0 |
Q8_0 | 8 | 15.0 GB | Very High | F0 |
F16 | 16 | 28.7 GB | Maximum | F0 |
Get started
Copy-paste commands to run GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV on your machine.
Run
lms load hf-srs6901--gguf-solarized-granistral-14b-2102-yeam-hct-32qkv && lms server start升级选项
能流畅运行 GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV 的硬件
RTX 3060 12GB经济之选RTX 5060 Ti 16GB最佳性价比16 GB VRAM (+5)
C
Removes host-memory offload, which is usually the single biggest latency and throughput win.31.5 tok/s 解码
Removes host-memory offload, which is usually the single biggest latency and throughput win.
Adds memory headroom for longer context windows and future model growth.
~$449 MSRP
RTX 4060 Ti 16GBNVIDIA 升级16 GB VRAM (+5)
C
Removes host-memory offload, which is usually the single biggest latency and throughput win.25.8 tok/s 解码
Removes host-memory offload, which is usually the single biggest latency and throughput win.
Adds memory headroom for longer context windows and future model growth.
~$499 MSRP
Frequently asked questions
Can RTX 2080 Ti 11GB run GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV?
Yes, RTX 2080 Ti 11GB can run GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV with a D grade (Very compromised (needs ~0.8 GB host RAM)). Expected decode speed: 27.3 tok/s.
How much VRAM does GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV need?
GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV (14B parameters) requires approximately 12.2 GB of memory with Q4_K_M quantization.
What is the best quantization for GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV?
The recommended quantization for GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV is Q4_K_M, which balances quality and memory efficiency.
What speed will GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV run at on RTX 2080 Ti 11GB?
On RTX 2080 Ti 11GB, GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV achieves approximately 27.3 tokens per second decode speed with a time-to-first-token of 7082ms using Q4_K_M quantization.
Can RTX 2080 Ti 11GB run GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV for coding?
For coding workloads, GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV on RTX 2080 Ti 11GB receives a D grade with 27.3 tok/s and 4K context.
What context window can GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV use on RTX 2080 Ti 11GB?
On RTX 2080 Ti 11GB, GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV can safely use up to 4K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
What should I upgrade first if GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV feels slow on RTX 2080 Ti 11GB?
Remove offload with more accelerator memory. Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.
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