RTX 3060 12GB经济之选12 GB VRAM (+4)360 GB/s (+40)
C
Raises estimated decode speed by about 26%.64 tok/s 解码
Raises estimated decode speed by about 26%.
Adds memory headroom for longer context windows and future model growth.
~$329 MSRP
Can falcon mamba 7b instruct Q4 K M run on GTX 1080 8GB?
YES — Tight Fit
C52Usable
Estimated from fit model
falcon mamba 7b instruct Q4 K M needs ~6.8 GB VRAM. GTX 1080 8GB has 8.0 GB. With Q4_K_M quantization, expect ~51 tok/s.
Runtime: llama.cppCapacity: TightBandwidth: LowStack: StandardBottleneck: Balanced
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) — 6.8 GB, 50.8 tok/s, Tight fit
6.8 GB required8.0 GB available
85% VRAM used
Fit status
Tight fit
Decode
50.8 tok/s
TTFT
3807 ms
Safe context
40K
Memory
6.8 GB / 8.0 GB
Memory breakdown
Weights4.3 GB
KV Cache0.8 GB
Runtime0.9 GB
Headroom0.8 GB
See how fast it feels
See how fast it feelsfalcon mamba 7b instruct Q4 K M on GTX 1080 8GB
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: 50.8 tok/s decode · 3.8s TTFT (warm) · 127 tok/s prefill
What limits this setup
This setup is broadly balanced for this model.
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
Performance by workload
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Runs well | 50.8 tok/s | 2077 ms | 40K |
| Coding | C | Tight fit | 50.8 tok/s | 3807 ms | 40K |
| Agentic Coding | C | Runs with offload | 50.8 tok/s | 5538 ms | 40K |
| Reasoning | C | Tight fit | 50.8 tok/s | 4500 ms | 40K |
| RAG | C | Runs with offload | 50.8 tok/s | 6923 ms | 40K |
Inference speed
falcon mamba 7b instruct Q4 K M inference speed — tokens per second by GPU & Mac
Estimated decode speed (tokens/sec) for falcon mamba 7b instruct Q4 K M 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 |
MacBook Pro M4 Max 128GB | 128 GB | Q4_K_M | 92.6 | Fits |
MacBook Pro M4 Max 64GB | 64 GB | Q4_K_M | 92.6 | Fits |
| RTX 4070 12GB | 12 GB | Q4_K_M | 88.5 | Fits |
MacBook Pro M3 Max 64GB | 64 GB | Q4_K_M | 64.6 | Fits |
MacBook Pro M1 Max 64GB | 64 GB | Q4_K_M | 59.3 | Fits |
MacBook Pro M4 Pro 48GB | 48 GB | Q4_K_M | 56.6 | Fits |
| RTX 3060 12GB | 12 GB | Q4_K_M | 55.6 | Fits |
| RTX 4060 8GB | 8 GB | Q4_K_M | 53.5 | Tight |
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.
Quantization options
How falcon mamba 7b instruct Q4 K M (7B params) fits at each quantization level on GTX 1080 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 | C53 |
Q6_K | 6 | 5.7 GB | High | F0 |
Q8_0 | 8 | 7.5 GB | Very High | F0 |
F16 | 16 | 14.3 GB | Maximum | F0 |
Get started
Copy-paste commands to run falcon mamba 7b instruct Q4 K M on your machine.
Run
lms load hf-tiiuae--falcon-mamba-7b-instruct-q4-k-m-gguf && lms server start升级选项
能流畅运行 falcon mamba 7b instruct Q4 K M 的硬件
RTX 3060 12GB经济之选RTX 5070 12GB最佳性价比12 GB VRAM (+4)672 GB/s (+352)
C
Raises estimated decode speed by about 124%.114 tok/s 解码
Raises estimated decode speed by about 124%.
Adds memory headroom for longer context windows and future model growth.
~$549 MSRP
RTX 4070 12GBNVIDIA 升级12 GB VRAM (+4)504 GB/s (+184)
C
Raises estimated decode speed by about 100%.101.8 tok/s 解码
Raises estimated decode speed by about 100%.
Adds memory headroom for longer context windows and future model growth.
~$599 MSRP
Frequently asked questions
Can GTX 1080 8GB run falcon mamba 7b instruct Q4 K M?
Yes, GTX 1080 8GB can run falcon mamba 7b instruct Q4 K M with a C grade (Tight fit). Expected decode speed: 50.8 tok/s.
How much VRAM does falcon mamba 7b instruct Q4 K M need?
falcon mamba 7b instruct Q4 K M (7B parameters) requires approximately 6.8 GB of memory with Q4_K_M quantization.
What is the best quantization for falcon mamba 7b instruct Q4 K M?
The recommended quantization for falcon mamba 7b instruct Q4 K M is Q4_K_M, which balances quality and memory efficiency.
What speed will falcon mamba 7b instruct Q4 K M run at on GTX 1080 8GB?
On GTX 1080 8GB, falcon mamba 7b instruct Q4 K M achieves approximately 50.8 tokens per second decode speed with a time-to-first-token of 3807ms using Q4_K_M quantization.
Can GTX 1080 8GB run falcon mamba 7b instruct Q4 K M for coding?
For coding workloads, falcon mamba 7b instruct Q4 K M on GTX 1080 8GB receives a C grade with 50.8 tok/s and 40K context.
What context window can falcon mamba 7b instruct Q4 K M use on GTX 1080 8GB?
On GTX 1080 8GB, falcon mamba 7b instruct Q4 K M can safely use up to 40K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
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