Integration guides · 2026-11-07
What is a 4-bit quantized model? Alpie Core memory and cost
What a 4-bit quantized model is, why weight memory falls to a quarter and how to check accuracy, plus running Alpie Core yourself versus calling it on LLMTR.
Short answer: a 4-bit quantized model stores each weight in 4 bits
LLMTR is a gateway that gives access to AI models from different providers through an OpenAI-compatible API. Alpie Core is 169Pi's 4-bit quantized reasoning model, called on LLMTR with the 169pi/alpie-core id at https://llmtr.com/v1/chat/completions using an API key created in your llmtr.com account and a loaded balance. A 4-bit quantized model stores each weight in 4 bits. Weight memory falls in proportion to the bit count, so against 16 bits it drops to a quarter. In exchange, precision falls, which can affect accuracy and has to be evaluated per task.
According to 169Pi's own pages, read on 7 November 2026, Alpie Core is a 32-billion-parameter reasoning model trained and served at 4-bit quantization. It is built on DeepSeek-R1-Distill-Qwen-32B and its weights are open under Apache 2.0. The Hugging Face page lists a minimum of 20 GB RAM/VRAM for local use. 169Pi's model page sums the model up as 'the full model, in a quarter of the memory'; that is the maker's own wording.
What is a 4-bit quantized model?
The weights of a language model are numbers, and each number takes up a certain count of bits in memory. The bit count sets how many distinct values a weight can hold: 16 bits can represent 65,536 values, 4 bits just 16. Quantization moves the weights onto that narrower set of values. The parameter count stays the same; each parameter takes less room.
For a developer choosing a model, this has two consequences. One is memory: the model loads with less RAM or VRAM. The other is accuracy risk: every rounded weight carries a small error, and the combined effect of those errors varies by model and by task. Quantization is often applied to a model after training, but it can also be used during training; 169Pi says Alpie Core was trained at 4-bit precision.
Memory arithmetic: how many GB do 32 billion weights take?
To find the weight memory, multiply the parameter count by the bit count and divide by 8; the result is in bytes. The list below does this with the 32 billion parameters 169Pi lists. 4 bits are a quarter of 16 bits, so the weight memory drops to a quarter too.
These figures cover roughly what the weights occupy and nothing else. A running model also needs memory for its context and for the runtime. So base a hardware decision on more than this arithmetic: on the minimum of 20 GB RAM/VRAM that 169Pi's Hugging Face page lists for local use, and on a trial on your own hardware.
- 16 bits: 32 billion × 16 / 8 = 64 GB
- 8 bits: 32 billion × 8 / 8 = 32 GB
- 4 bits: 32 billion × 4 / 8 = 16 GB
Accuracy risk: how do you check it on your own task?
A general figure for how quantization affects accuracy says little about your work. Test with your own data: prepare a small set of real questions whose answers you know, write each question with its context, and compare the replies with the expected answers. On very short prompts with no context the answer can be off-topic, so write the questions in full. If you want a Turkish answer, end your prompt with "Türkçe yanıtla."; without it the answer can come back in English.
The example below runs that check. It sends each question to 169pi/alpie-core, treats a reply whose finish_reason is length as incomplete, looks for the expected answer in the content field and adds up the cost from the usage field. max_tokens is kept high because the model reasons before every answer and the reasoning text is often longer than the answer. There is no tool calling and no JSON mode, so describe the format of the reply in the prompt and validate it yourself. On maths questions the result can arrive wrapped in \boxed{}; the search in the example still finds it there, and your own parser should allow for it too. The model can get arithmetic wrong. Check numeric results in your own code. For streaming and the response fields, see the series article Alpie Core API: first request with LLMTR.
OpenAI Python SDK example that sends questions with known answers to Alpie Core through https://llmtr.com/v1/chat/completions, compares the replies and adds up the cost from the usage field.
import os
from openai import OpenAI
client = OpenAI(base_url="https://llmtr.com/v1", api_key=os.environ["LLMTR_API_KEY"])
MODEL = "169pi/alpie-core"
INPUT_USD_PER_1M = 0.15
OUTPUT_USD_PER_1M = 0.40
# Replace these with real questions from your own work and the answers you expect.
CASES = [
(
"An order has 3 boxes of 12 items and 2 boxes of 8 items, and 5 items are damaged. "
"How many undamaged items are there? End your answer with the number.",
"47",
),
(
"A function is called 3 times per request and each call runs 4 database queries. "
"How many queries do 250 requests run? End your answer with the number.",
"3000",
),
]
passed = 0
cost_usd = 0.0
for question, expected in CASES:
response = client.chat.completions.create(
model=MODEL,
messages=[{"role": "user", "content": question}],
max_tokens=8192,
)
usage = response.usage
cost_usd += (usage.prompt_tokens * INPUT_USD_PER_1M + usage.completion_tokens * OUTPUT_USD_PER_1M) / 1_000_000
choice = response.choices[0]
# A finish_reason of "length" means the reply is incomplete: content can be partial or empty.
if choice.finish_reason == "length":
print("incomplete:", question[:50])
elif expected in (choice.message.content or "").replace(",", ""):
passed += 1
else:
print("check by hand:", question[:50])
print(passed, "of", len(CASES), "matched, cost in USD:", round(cost_usd, 6))Run the weights yourself, or call the model on LLMTR?
The weights are open, so both routes exist. Running them yourself makes sense when you want to modify or fine-tune the weights, or when an organizational requirement says the model must run on your own hardware. In return, the hardware, the runtime and the upkeep are yours.
Calling it on LLMTR makes sense when you want to see the model on your own work without setting up hardware. You can also try it through the API and decide on hardware afterwards, with your own results in hand. The table shows what you must provide and what you get on each route.
| Topic | If you run it yourself | If you call it on LLMTR |
|---|---|---|
| What you provide | hardware with at least 20 GB RAM/VRAM (169Pi's Hugging Face page), the runtime setup and upkeep | an LLMTR API key and a loaded balance; no separate 169Pi account |
| What you get | control over Apache 2.0 weights: you can modify and fine-tune them | the OpenAI-compatible address https://llmtr.com/v1/chat/completions |
| How cost works | hardware, energy and the time of whoever runs it; these continue with no requests | $0.15 input and $0.40 output per 1M tokens; reasoning counts as output |
| Limits | depend on your runtime settings and your hardware | 65,000-token context, up to 16,384 tokens per response, text input; no tool calling or JSON mode |
| Seeing usage | you set up monitoring yourself | the usage field in the response and the Dashboard > Usage page |
Memory and cost: how do you work out the sum?
Quantization affects the memory you need when you run the model yourself. When you call it on LLMTR you plan no memory; what you pay for is tokens. The price is $0.15 input and $0.40 output per 1M tokens, with no separate cache price. Reasoning tokens are billed as output.
Take one assumption: a 1,000-token prompt and 3,000 output tokens, reasoning included, come to $0.00015 of input and $0.0012 of output, so the request costs $0.00135. At that size, 100,000 requests cost $135. For your own sum, take the averages from the usage field, multiply by your monthly request count and set the result beside the monthly cost of hardware, energy and upkeep. The comparison depends on your work and your hardware, so this article does not give you a result.
Summary for your team
Recommendation: to see how Alpie Core, a 4-bit quantized model, handles your own work, try it on llmtr.com with the id 169pi/alpie-core at https://llmtr.com/v1/chat/completions. The price is $0.15 input and $0.40 output per 1M tokens as of 7 November 2026, and the context window is 65,000 tokens.
The reasons: you can start without setting up hardware; one LLMTR API key is enough and no separate 169Pi account is needed. A loaded balance is required to make calls. Usage appears on the Dashboard > Usage page, so you can see what the trial cost there and discuss running the weights yourself with those numbers in hand.
Check a 4-bit quantized model on your own task
Prepare a small set of questions whose answers you know, send them to 169pi/alpie-core, compare the replies and work out the cost from the usage field.
- Set. Prepare a small set of real questions whose answers you know and write each question with its context.
- Request. Send each question with the OpenAI Python SDK to https://llmtr.com/v1/chat/completions with model set to 169pi/alpie-core; do not set max_tokens low.
- Decide. Compare the replies with the expected answers, treat a finish_reason of length as incomplete, work out the cost from the usage field and decide with those results.
Frequently asked questions
What is a 4-bit quantized model?
It is a model that stores each weight in 4 bits. The parameter count stays the same and the weight memory falls to a quarter against 16 bits; in exchange, precision falls and accuracy has to be evaluated per task.
How much memory do you need to run Alpie Core locally?
169Pi's Hugging Face page lists a minimum of 20 GB RAM/VRAM for local use. If you do not want to set up hardware, you can send a request with the 169pi/alpie-core id to https://llmtr.com/v1/chat/completions using your LLMTR API key.
Does a 4-bit model lose accuracy?
It can; the effect varies by model and by task. Test with your own data, using real questions whose answers you know, and judge the result for your own task.