GTX 1660 Super 6GBGünstige Wahl6 GB VRAM (+2)336 GB/s (+144)
B
Adds memory headroom for longer context windows and future model growth.25.8 tok/s Dekodierung
Adds memory headroom for longer context windows and future model growth.
ca. $229 MSRP
Can Antares 1B run on RTX 3050 Ti Laptop 4GB?
YES — With Offload
B67Good
Estimated from fit model
Antares 1B needs ~3.9 GB VRAM. RTX 3050 Ti Laptop 4GB has 4.0 GB. With Q4_K_M quantization, expect ~26 tok/s.
Runtime: OllamaCapacity: OffloadBandwidth: Very 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
Q4_K_M (Medium quality) — 3.9 GB, 25.8 tok/s, Runs with offload
3.9 GB required4.0 GB available
98% VRAM used
Fit status
Runs with offload
Decode
25.8 tok/s
TTFT
7516 ms
Safe context
17K
Memory
3.9 GB / 4.0 GB
Memory breakdown
Weights1.1 GB
KV Cache1.2 GB
Runtime1.2 GB
Headroom0.4 GB
See how fast it feels
See how fast it feelsAntares 1B on RTX 3050 Ti Laptop 4GB
1st promptCold start — includes initialization
>Write a Python function to sort a list
Here's a Python function that sorts a list using quicksort:
def quicksort(arr):
if len(arr) <= 1:
return arr
pivot = arr[len(arr) // 2]
left = [x for x in arr if x < pivot]
middle = [x for x in arr if x == pivot]
right = [x for x in arr if x > pivot]
return quicksort(left) + middle + quicksort(right)
# Example usage
numbers = [3, 6, 8, 10, 1, 2, 1]
print(quicksort(numbers)) # [1, 1, 2, 3, 6, 8, 10]
The algorithm picks a pivot, partitions the list into smaller, equal, and larger elements, then recursively sorts each partition and concatenates the results.
2nd promptWarm — model ready, no init overhead
>Now add type hints and a docstring
def quicksort(arr: list[int]) -> list[int]:
"""Sort a list using the quicksort algorithm.
Args:
arr: List of integers to sort.
Returns:
A new sorted list.
"""
if len(arr) <= 1:
return arr
pivot = arr[len(arr) // 2]
left = [x for x in arr if x < pivot]
middle = [x for x in arr if x == pivot]
right = [x for x in arr if x > pivot]
return quicksort(left) + middle + quicksort(right)
Added type hints for the input and return type, plus a Google-style docstring.
Estimated: 25.8 tok/s decode · 7.5s TTFT (warm) · 64 tok/s prefill
What limits this setup
This setup is broadly balanced for this model.
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.
Best improvement path
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
Performance by workload
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | B | Tight fit | 25.8 tok/s | 4099 ms | 17K |
| Coding | B | Runs with offload | 25.8 tok/s | 7516 ms | 17K |
| Agentic Coding | F | Too heavy | 25.8 tok/s | 10932 ms | 17K |
| Reasoning | B | Runs with offload | 25.8 tok/s | 8882 ms | 17K |
| RAG | F | Too heavy | 25.8 tok/s | 13665 ms | 17K |
Inference speed
Antares 1B inference speed — tokens per second by GPU & Mac
Estimated decode speed (tokens/sec) for Antares 1B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~35 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 | 35.0 | Fits |
| RTX 4090 24GB | 24 GB | Q4_K_M | 29.4 | Fits |
| RTX 4080 Super 16GB | 16 GB | Q4_K_M | 29.4 | Fits |
| RTX 3090 24GB | 24 GB | Q4_K_M | 25.8 | Fits |
| RTX 4070 12GB | 12 GB | Q4_K_M | 25.8 | Fits |
| RTX 3060 12GB | 12 GB | Q4_K_M | 25.8 | Fits |
| RTX 4060 8GB | 8 GB | Q4_K_M | 25.8 | Fits |
RX 7900 XTX 24GB | 24 GB | Q4_K_M | 25.8 | Fits |
MacBook Pro M4 Max 128GB | 128 GB | Q4_K_M | 25.8 | Fits |
Mac Studio M3 Ultra 256GB | 256 GB | Q4_K_M | 25.8 | Fits |
Mac Studio M2 Ultra 128GB | 128 GB | Q4_K_M | 25.8 | Fits |
Mac Studio M1 Ultra 128GB | 128 GB | Q4_K_M | 25.8 | Fits |
MacBook Pro M4 Max 64GB | 64 GB | Q4_K_M | 25.8 | Fits |
MacBook Pro M3 Max 64GB | 64 GB | Q4_K_M | 25.8 | Fits |
MacBook Pro M1 Max 64GB | 64 GB | Q4_K_M | 25.8 | Fits |
MacBook Pro M4 Pro 48GB | 48 GB | Q4_K_M | 25.8 | 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 Antares 1B (1.840000033378601B params) fits at each quantization level on RTX 3050 Ti Laptop 4GB (4.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q1_0_G128 | 1.125 | 0.3 GB | Very Low | A72 |
Q2_0_G128 | 1.71 | 0.5 GB | Low | A73 |
Q2_K | 2 | 0.7 GB | Low | A73 |
Q3_K_S | 3 | 0.9 GB | Low | A73 |
NVFP4 | 4 | 1.0 GB | Medium | A73 |
Q4_K_M | 4 | 1.1 GB | Medium | A73 |
Q5_K_M | 5 | 1.3 GB | High | A72 |
Q6_KBest for your GPU | 6 | 1.5 GB | High | A72 |
Q8_0 | 8 | 2.0 GB | Very High | F0 |
F16 | 16 | 3.8 GB | Maximum | F0 |
Get started
Copy-paste commands to run Antares 1B on your machine.
Run
docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
--hf-repo "fdtn-ai/antares-1b" \
--hf-file "antares-1b-Q4_K_M.gguf" \
-c 4096 -ngl 99Upgrade-Optionen
Hardware, die Antares 1B gut ausführt
GTX 1660 Super 6GBGünstige WahlGTX 1060 6GBBestes Preis-Leistungs-Verhältnis6 GB VRAM (+2)
B
Adds memory headroom for longer context windows and future model growth.25.8 tok/s Dekodierung
Adds memory headroom for longer context windows and future model growth.
ca. $249 MSRP
GTX 1660 Ti 6GBNVIDIA-Upgrade6 GB VRAM (+2)288 GB/s (+96)
B
Adds memory headroom for longer context windows and future model growth.25.8 tok/s Dekodierung
Adds memory headroom for longer context windows and future model growth.
ca. $279 MSRP
Frequently asked questions
Can RTX 3050 Ti Laptop 4GB run Antares 1B?
Yes, RTX 3050 Ti Laptop 4GB can run Antares 1B with a B grade (Runs with offload). Expected decode speed: 25.8 tok/s.
How much VRAM does Antares 1B need?
Antares 1B (1.840000033378601B parameters) requires approximately 3.9 GB of memory with Q4_K_M quantization.
What is the best quantization for Antares 1B?
The recommended quantization for Antares 1B is Q4_K_M, which balances quality and memory efficiency.
What speed will Antares 1B run at on RTX 3050 Ti Laptop 4GB?
On RTX 3050 Ti Laptop 4GB, Antares 1B achieves approximately 25.8 tokens per second decode speed with a time-to-first-token of 7516ms using Q4_K_M quantization.
Can RTX 3050 Ti Laptop 4GB run Antares 1B for coding?
For coding workloads, Antares 1B on RTX 3050 Ti Laptop 4GB receives a B grade with 25.8 tok/s and 17K context.
What context window can Antares 1B use on RTX 3050 Ti Laptop 4GB?
On RTX 3050 Ti Laptop 4GB, Antares 1B can safely use up to 17K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.
What should I upgrade first if Antares 1B feels slow on RTX 3050 Ti Laptop 4GB?
Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
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