MacBook Pro M4 32GBコスパ重視
32 GB Unified (+12)
A
The model fits in shared memory, but shared-memory bandwidth is now the real limiter.4.7 tok/sデコード
〜$799 MSRP
Can BGE Large EN v1.5 run on RTX 4000 Ada 20GB?
YES — Runs Great
B70Good
Estimated from fit model
BGE Large EN v1.5 needs ~5.4 GB VRAM. RTX 4000 Ada 20GB has 20.0 GB. With F16 quantization, expect ~5 tok/s.
Runtime: OllamaCapacity: RoomyBandwidth: LowStack: BasicBottleneck: Memory bandwidth
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
F16 (Maximum quality) — 5.4 GB, 4.7 tok/s, Runs well
5.4 GB required20.0 GB available
27% VRAM used
Fit status
Runs well
Decode
4.7 tok/s
TTFT
41279 ms
Safe context
512
Memory
5.4 GB / 20.0 GB
Memory breakdown
Weights0.7 GB
KV Cache1.5 GB
Runtime1.2 GB
Headroom2.0 GB
See how fast it feels
See how fast it feelsBGE Large EN v1.5 on RTX 4000 Ada 20GB
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: 4.7 tok/s decode · 41.3s TTFT (warm) · 12 tok/s prefill
What limits this setup
This model fits, but memory bandwidth is the part holding decode speed back.
Throughput will feel slow
Estimated decode speed is only 4.7 tok/s, so this is more of a technical fit than a comfortable daily-driver setup.
Best improvement path
Prioritize bandwidth, not only capacity
If this workload feels slow, the next useful step is often a GPU tier with materially faster memory bandwidth rather than only a small bump in capacity.
Performance by workload
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | B | Runs well | 4.7 tok/s | 22516 ms | 512 |
| Coding | B | Runs well | 4.7 tok/s | 41279 ms | 512 |
| Agentic Coding | A | Runs well | 4.7 tok/s | 60043 ms | 512 |
| Reasoning | B | Runs well | 4.7 tok/s | 48785 ms | 512 |
| RAG | A | Runs well | 4.7 tok/s | 75053 ms | 512 |
Quantization options
How BGE Large EN v1.5 (0.33500000834465027B params) fits at each quantization level on RTX 4000 Ada 20GB (20.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 0.1 GB | Low | A76 |
Q3_K_S | 3 | 0.2 GB | Low | A76 |
NVFP4 | 4 | 0.2 GB | Medium | A76 |
Q4_K_M | 4 | 0.2 GB | Medium | A76 |
Q5_K_M | 5 | 0.2 GB | High | A76 |
Q6_K | 6 | 0.3 GB | High | A76 |
Q8_0 | 8 | 0.4 GB | Very High | A76 |
F16Best for your GPU | 16 | 0.7 GB | Maximum | A76 |
Get started
Copy-paste commands to run BGE Large EN v1.5 on your machine.
Run
docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
--hf-repo "BAAI/bge-large-en-v1.5" \
--hf-file "bge-large-en-v1.5-F16.gguf" \
-c 4096 -ngl 99アップグレードオプション
BGE Large EN v1.5を快適に動かすハードウェア
Frequently asked questions
Can RTX 4000 Ada 20GB run BGE Large EN v1.5?
Yes, RTX 4000 Ada 20GB can run BGE Large EN v1.5 with a B grade (Runs well). Expected decode speed: 4.7 tok/s.
How much VRAM does BGE Large EN v1.5 need?
BGE Large EN v1.5 (0.33500000834465027B parameters) requires approximately 5.4 GB of memory with F16 quantization.
What is the best quantization for BGE Large EN v1.5?
The recommended quantization for BGE Large EN v1.5 is F16, which balances quality and memory efficiency.
What speed will BGE Large EN v1.5 run at on RTX 4000 Ada 20GB?
On RTX 4000 Ada 20GB, BGE Large EN v1.5 achieves approximately 4.7 tokens per second decode speed with a time-to-first-token of 41279ms using F16 quantization.
Can RTX 4000 Ada 20GB run BGE Large EN v1.5 for coding?
For coding workloads, BGE Large EN v1.5 on RTX 4000 Ada 20GB receives a B grade with 4.7 tok/s and 512 context.
What context window can BGE Large EN v1.5 use on RTX 4000 Ada 20GB?
On RTX 4000 Ada 20GB, BGE Large EN v1.5 can safely use up to 512 tokens of context. The model's official context limit is 512, but available memory constrains the safe maximum.
What should I upgrade first if BGE Large EN v1.5 feels slow on RTX 4000 Ada 20GB?
Prioritize bandwidth, not only capacity. If this workload feels slow, the next useful step is often a GPU tier with materially faster memory bandwidth rather than only a small bump in capacity.
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