MacBook Pro M3 Max 128GBGünstige Wahl
128 GB Unified (+48)
C
This setup is broadly balanced for this model.42 tok/s Dekodierung
ca. $2,499 MSRP
Can ai21labs AI21 Jamba Reasoning 3B run on NVIDIA A100 80GB?
YES — Runs Great
C42Usable
Estimated from fit model
ai21labs AI21 Jamba Reasoning 3B needs ~11.4 GB VRAM. NVIDIA A100 80GB has 80.0 GB. With Q4_K_M quantization, expect ~42 tok/s.
Runtime: OllamaCapacity: RoomyBandwidth: HighStack: BasicBottleneck: 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) — 11.4 GB, 42.0 tok/s, Runs well
11.4 GB required80.0 GB available
14% VRAM used
Fit status
Runs well
Decode
42.0 tok/s
TTFT
4610 ms
Safe context
3.1M
Memory
11.4 GB / 80.0 GB
Memory breakdown
Weights1.8 GB
KV Cache0.4 GB
Runtime1.2 GB
Headroom8.0 GB
See how fast it feels
See how fast it feelsai21labs AI21 Jamba Reasoning 3B on NVIDIA A100 80GB
1st promptCold start — includes initialization
>Solve: If a train travels at 60 mph for 2.5 hours, how far does it go?
Let me work through this step by step.
Given information:
- Speed = 60 mph (miles per hour)
- Time = 2.5 hours
Formula: Distance = Speed x Time
Calculation:
Distance = 60 mph x 2.5 h
= 150 miles
The train travels 150 miles.
Double-check: at 60 mph the train covers 60 miles each hour. In half an hour it covers 30 miles. So in 2.5 hours: 60 + 60 + 30 = 150 miles. Confirmed.
2nd promptWarm — model ready, no init overhead
>What if the train stops for 20 minutes halfway?
The stop doesn't change the total distance covered while moving, but it affects total trip time.
Breakdown:
- First half: 1.25 hours at 60 mph = 75 miles
- Stop: 20 minutes (no distance)
- Second half: 1.25 hours at 60 mph = 75 miles
Total distance: 150 miles (same)
Total time: 2.5 h + 0.33 h = 2.83 hours
Average speed: 150 / 2.83 = 53 mph
The distance stays the same but average speed drops to 53 mph because of the stop.
Estimated: 42.0 tok/s decode · 4.6s TTFT (warm) · 105 tok/s prefill
What limits this setup
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.
Best improvement path
Performance by workload
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Runs well | 42.0 tok/s | 2514 ms | 3.1M |
| Coding | C | Runs well | 42.0 tok/s | 4610 ms | 3.1M |
| Agentic Coding | C | Runs well | 42.0 tok/s | 6705 ms | 3.1M |
| Reasoning | C | Runs well | 42.0 tok/s | 5448 ms | 3.1M |
| RAG | C | Runs well | 42.0 tok/s | 8381 ms | 3.1M |
Inference speed
ai21labs AI21 Jamba Reasoning 3B inference speed — tokens per second by GPU & Mac
Estimated decode speed (tokens/sec) for ai21labs AI21 Jamba Reasoning 3B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~57 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 | 57.0 | Fits |
| RTX 4090 24GB | 24 GB | Q4_K_M | 48.0 | Fits |
| RTX 4080 Super 16GB | 16 GB | Q4_K_M | 48.0 | Fits |
| RTX 3090 24GB | 24 GB | Q4_K_M | 42.0 | Fits |
| RTX 4070 12GB | 12 GB | Q4_K_M | 42.0 | Fits |
| RTX 3060 12GB | 12 GB | Q4_K_M | 42.0 | Fits |
| RTX 4060 8GB | 8 GB | Q4_K_M | 42.0 | Fits |
RX 7900 XTX 24GB | 24 GB | Q4_K_M | 42.0 | Fits |
MacBook Pro M4 Max 128GB | 128 GB | Q4_K_M | 42.0 | Fits |
Mac Studio M3 Ultra 256GB | 256 GB | Q4_K_M | 42.0 | Fits |
Mac Studio M2 Ultra 128GB | 128 GB | Q4_K_M | 42.0 | Fits |
Mac Studio M1 Ultra 128GB | 128 GB | Q4_K_M | 42.0 | Fits |
MacBook Pro M4 Max 64GB | 64 GB | Q4_K_M | 42.0 | Fits |
MacBook Pro M3 Max 64GB | 64 GB | Q4_K_M | 42.0 | Fits |
MacBook Pro M1 Max 64GB | 64 GB | Q4_K_M | 42.0 | Fits |
MacBook Pro M4 Pro 48GB | 48 GB | Q4_K_M | 42.0 | 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.
Quantization options
How ai21labs AI21 Jamba Reasoning 3B (3B params) fits at each quantization level on NVIDIA A100 80GB (80.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 1.2 GB | Low | D39 |
Q3_K_S | 3 | 1.5 GB | Low | D39 |
NVFP4 | 4 | 1.7 GB | Medium | D39 |
Q4_K_M | 4 | 1.8 GB | Medium | D39 |
Q5_K_M | 5 | 2.2 GB | High | D39 |
Q6_K | 6 | 2.5 GB | High | D39 |
Q8_0 | 8 | 3.2 GB | Very High | D39 |
F16Best for your GPU | 16 | 6.1 GB | Maximum | D39 |
Get started
Copy-paste commands to run ai21labs AI21 Jamba Reasoning 3B on your machine.
Run
lms load hf-bartowski--ai21labs-ai21-jamba-reasoning-3b-gguf && lms server startUpgrade-Optionen
Hardware, die ai21labs AI21 Jamba Reasoning 3B gut ausführt
Mac Studio M2 Ultra 128GBBestes Preis-Leistungs-Verhältnis
128 GB Unified (+48)
C
This setup is broadly balanced for this model.42 tok/s Dekodierung
ca. $3,999 MSRP
NVIDIA DGX Spark 128GBNVIDIA-Upgrade128 GB Unified (+48)
C
Adds memory headroom for longer context windows and future model growth.42 tok/s Dekodierung
Adds memory headroom for longer context windows and future model growth.
Frequently asked questions
Can NVIDIA A100 80GB run ai21labs AI21 Jamba Reasoning 3B?
Yes, NVIDIA A100 80GB can run ai21labs AI21 Jamba Reasoning 3B with a C grade (Runs well). Expected decode speed: 42.0 tok/s.
How much VRAM does ai21labs AI21 Jamba Reasoning 3B need?
ai21labs AI21 Jamba Reasoning 3B (3B parameters) requires approximately 11.4 GB of memory with Q4_K_M quantization.
What is the best quantization for ai21labs AI21 Jamba Reasoning 3B?
The recommended quantization for ai21labs AI21 Jamba Reasoning 3B is Q4_K_M, which balances quality and memory efficiency.
What speed will ai21labs AI21 Jamba Reasoning 3B run at on NVIDIA A100 80GB?
On NVIDIA A100 80GB, ai21labs AI21 Jamba Reasoning 3B achieves approximately 42.0 tokens per second decode speed with a time-to-first-token of 4610ms using Q4_K_M quantization.
Can NVIDIA A100 80GB run ai21labs AI21 Jamba Reasoning 3B for coding?
For coding workloads, ai21labs AI21 Jamba Reasoning 3B on NVIDIA A100 80GB receives a C grade with 42.0 tok/s and 3.1M context.
What context window can ai21labs AI21 Jamba Reasoning 3B use on NVIDIA A100 80GB?
On NVIDIA A100 80GB, ai21labs AI21 Jamba Reasoning 3B can safely use up to 3.1M tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
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